Overview

Dataset statistics

Number of variables331
Number of observations546643
Missing cells79480128
Missing cells (%)43.9%
Total size in memory1.3 GiB
Average record size in memory2.6 KiB

Variable types

DateTime1
Numeric1
Categorical327
Unsupported2

Alerts

UmfrageName has constant value "kuzu_zug"Constant
BHF_park has a high cardinality: 145 distinct valuesHigh cardinality
BHF_platz has a high cardinality: 425 distinct valuesHigh cardinality
BHF_sauberkeit has a high cardinality: 502 distinct valuesHigh cardinality
BHF_umsteig has a high cardinality: 555 distinct valuesHigh cardinality
BHF_velo has a high cardinality: 159 distinct valuesHigh cardinality
BHF_wc has a high cardinality: 69 distinct valuesHigh cardinality
BHF_wegweisung has a high cardinality: 441 distinct valuesHigh cardinality
createdAt has a high cardinality: 461329 distinct valuesHigh cardinality
date_of_first_mail has a high cardinality: 47624 distinct valuesHigh cardinality
date_of_last_access has a high cardinality: 378420 distinct valuesHigh cardinality
EKL_fehlende_infos_txt has a high cardinality: 693 distinct valuesHigh cardinality
fg_abfahrt has a high cardinality: 1400 distinct valuesHigh cardinality
fg_ankunft has a high cardinality: 1416 distinct valuesHigh cardinality
fg_startort has a high cardinality: 16108 distinct valuesHigh cardinality
fg_startort_uic has a high cardinality: 14385 distinct valuesHigh cardinality
fg_via has a high cardinality: 50375 distinct valuesHigh cardinality
fg_vm has a high cardinality: 159831 distinct valuesHigh cardinality
fg_zielort has a high cardinality: 15909 distinct valuesHigh cardinality
fg_zielort_uic has a high cardinality: 14161 distinct valuesHigh cardinality
ft_abfahrt has a high cardinality: 1394 distinct valuesHigh cardinality
ft_ankunft has a high cardinality: 1407 distinct valuesHigh cardinality
ft_haltestellen has a high cardinality: 25283 distinct valuesHigh cardinality
ft_startort has a high cardinality: 7705 distinct valuesHigh cardinality
ft_startort_uic has a high cardinality: 2843 distinct valuesHigh cardinality
ft_tu has a high cardinality: 60 distinct valuesHigh cardinality
ft_uic_haltestellen has a high cardinality: 33431 distinct valuesHigh cardinality
ft_vm has a high cardinality: 31234 distinct valuesHigh cardinality
ft_zielort has a high cardinality: 6649 distinct valuesHigh cardinality
ft_zielort_uic has a high cardinality: 1836 distinct valuesHigh cardinality
ft_zug_nr has a high cardinality: 20127 distinct valuesHigh cardinality
Kommentar has a high cardinality: 146839 distinct valuesHigh cardinality
OES_grund_beeintraecht_other_txt has a high cardinality: 3726 distinct valuesHigh cardinality
OES_grund_personal_negativ_txt has a high cardinality: 874 distinct valuesHigh cardinality
participant_id has a high cardinality: 546643 distinct valuesHigh cardinality
projectLfn has a high cardinality: 224271 distinct valuesHigh cardinality
R_grund_nonuse_5txt has a high cardinality: 2080 distinct valuesHigh cardinality
R_Park has a high cardinality: 310 distinct valuesHigh cardinality
R_platz_andere_txt has a high cardinality: 680 distinct valuesHigh cardinality
R_sauber_anderes_txt has a high cardinality: 302 distinct valuesHigh cardinality
R_umsteig_andere_txt has a high cardinality: 1256 distinct valuesHigh cardinality
R_unzuf_comfort_txt has a high cardinality: 16840 distinct valuesHigh cardinality
R_unzuf_fahrplan_txt has a high cardinality: 6690 distinct valuesHigh cardinality
R_unzuf_gastro_ambiente_txt has a high cardinality: 156 distinct valuesHigh cardinality
R_unzuf_gastro_auswahl_txt has a high cardinality: 179 distinct valuesHigh cardinality
R_unzuf_gastro_kompetenz_txt has a high cardinality: 71 distinct valuesHigh cardinality
R_unzuf_gastro_preis_txt has a high cardinality: 268 distinct valuesHigh cardinality
R_unzuf_info_txt has a high cardinality: 3427 distinct valuesHigh cardinality
R_unzuf_mobile_txt has a high cardinality: 5672 distinct valuesHigh cardinality
R_unzuf_platzangebot_txt has a high cardinality: 9636 distinct valuesHigh cardinality
R_unzuf_preis_txt has a high cardinality: 13736 distinct valuesHigh cardinality
R_unzuf_gastro_quality_txt has a high cardinality: 99 distinct valuesHigh cardinality
R_unzuf_puenktlichkeit_txt has a high cardinality: 6005 distinct valuesHigh cardinality
R_unzuf_Sauberkeit_Bhf_txt has a high cardinality: 2556 distinct valuesHigh cardinality
R_unzuf_stoerungsinfo_txt has a high cardinality: 3767 distinct valuesHigh cardinality
R_unzuf_wc_avail_txt has a high cardinality: 6103 distinct valuesHigh cardinality
R_unzuf_wc_clean_txt has a high cardinality: 4607 distinct valuesHigh cardinality
R_unzuf_Wegweisung_Bhf_txt has a high cardinality: 2420 distinct valuesHigh cardinality
R_unzuf_zug_clean_txt has a high cardinality: 11326 distinct valuesHigh cardinality
R_unzuf_zugpers_txt has a high cardinality: 3210 distinct valuesHigh cardinality
R_Velo has a high cardinality: 680 distinct valuesHigh cardinality
R_WC has a high cardinality: 120 distinct valuesHigh cardinality
R_weg_andere_txt has a high cardinality: 714 distinct valuesHigh cardinality
RF_kanal_other_txt has a high cardinality: 2299 distinct valuesHigh cardinality
RF_mob_andere_txt has a high cardinality: 483 distinct valuesHigh cardinality
RF_webs_andere_txt has a high cardinality: 72 distinct valuesHigh cardinality
RF_zufallsitem1_label has a high cardinality: 696 distinct valuesHigh cardinality
S_AB7txt has a high cardinality: 4849 distinct valuesHigh cardinality
S_alter has a high cardinality: 91 distinct valuesHigh cardinality
SF_kanal_other_txt has a high cardinality: 1788 distinct valuesHigh cardinality
tag_zug_nr has a high cardinality: 416276 distinct valuesHigh cardinality
u_artikel has a high cardinality: 247 distinct valuesHigh cardinality
SF_unzuf_info_txt has a high cardinality: 1511 distinct valuesHigh cardinality
u_date has a high cardinality: 2111 distinct valuesHigh cardinality
u_hindatum has a high cardinality: 2111 distinct valuesHigh cardinality
u_kaufdatum has a high cardinality: 2144 distinct valuesHigh cardinality
u_preis has a high cardinality: 2858 distinct valuesHigh cardinality
updatedAt has a high cardinality: 461329 distinct valuesHigh cardinality
wime_unzuf_sf_txt has a high cardinality: 1608 distinct valuesHigh cardinality
SF_kanal_zuf is highly imbalanced (84.8%)Imbalance
wime_wc is highly imbalanced (75.0%)Imbalance
wime_sf_behebung is highly imbalanced (82.8%)Imbalance
wime_wc_verfuegb is highly imbalanced (72.1%)Imbalance
wime_gastro_ambiente is highly imbalanced (87.2%)Imbalance
wime_gastro_auswahl is highly imbalanced (87.2%)Imbalance
wime_gastro_freundlich is highly imbalanced (87.7%)Imbalance
wime_gastro_kompetenz is highly imbalanced (87.6%)Imbalance
wime_gastro_preis is highly imbalanced (87.1%)Imbalance
wime_gastro_qualitaet is highly imbalanced (87.3%)Imbalance
BHF_park is highly imbalanced (99.3%)Imbalance
BHF_platz is highly imbalanced (94.8%)Imbalance
BHF_sauberkeit is highly imbalanced (95.7%)Imbalance
BHF_share is highly imbalanced (99.9%)Imbalance
BHF_umsteig is highly imbalanced (94.7%)Imbalance
BHF_velo is highly imbalanced (98.6%)Imbalance
BHF_wc is highly imbalanced (99.6%)Imbalance
BHF_wegweisung is highly imbalanced (96.3%)Imbalance
EKL_fehlende_infos_txt is highly imbalanced (99.5%)Imbalance
EKL_info_nutzung is highly imbalanced (96.3%)Imbalance
EKL_info_vermisst is highly imbalanced (94.3%)Imbalance
EKL_info_zielerreichung is highly imbalanced (90.0%)Imbalance
EKL_reiseaenderung is highly imbalanced (76.5%)Imbalance
OES_zuf_personal_angemessen is highly imbalanced (69.2%)Imbalance
OES_zuf_personal_aufmerksam is highly imbalanced (68.2%)Imbalance
OES_zuf_personal_effekt is highly imbalanced (67.3%)Imbalance
Wime_Sicherheit_BhfStart is highly imbalanced (84.0%)Imbalance
Wime_WC_BhfStart is highly imbalanced (97.6%)Imbalance
Wime_Velo_BhfStart is highly imbalanced (91.6%)Imbalance
Wime_Park_BhfStart is highly imbalanced (91.3%)Imbalance
Wime_Share_BhfStart is highly imbalanced (99.0%)Imbalance
Wime_Sicherheit_BhfZiel is highly imbalanced (86.3%)Imbalance
Wime_WC_BhfZiel is highly imbalanced (97.6%)Imbalance
Wime_Velo_BhfZiel is highly imbalanced (96.2%)Imbalance
Wime_Park_BhfZiel is highly imbalanced (96.1%)Imbalance
Wime_Share_BhfZiel is highly imbalanced (99.2%)Imbalance
wime_sicherheitskraft is highly imbalanced (93.5%)Imbalance
fg_via is highly imbalanced (51.5%)Imbalance
ft_tu is highly imbalanced (70.0%)Imbalance
Kommentar is highly imbalanced (64.0%)Imbalance
OES_beeintraechtigung is highly imbalanced (78.2%)Imbalance
OES_grund_beeintraecht_1 is highly imbalanced (85.1%)Imbalance
OES_grund_beeintraecht_2 is highly imbalanced (85.2%)Imbalance
OES_grund_beeintraecht_3 is highly imbalanced (85.5%)Imbalance
OES_grund_beeintraecht_4 is highly imbalanced (85.3%)Imbalance
OES_grund_beeintraecht_5 is highly imbalanced (86.9%)Imbalance
OES_grund_beeintraecht_6 is highly imbalanced (87.6%)Imbalance
OES_grund_beeintraecht_7 is highly imbalanced (97.2%)Imbalance
OES_grund_beeintraecht_7_txt is highly imbalanced (> 99.9%)Imbalance
OES_grund_beeintraecht_other is highly imbalanced (85.2%)Imbalance
OES_grund_beeintraecht_other_txt is highly imbalanced (98.3%)Imbalance
OES_grund_personal_negativ_txt is highly imbalanced (99.6%)Imbalance
OES_personal_zug is highly imbalanced (54.1%)Imbalance
R_abo_datum is highly imbalanced (77.8%)Imbalance
R_abo_nutzung is highly imbalanced (69.7%)Imbalance
R_abotk_klasse is highly imbalanced (72.0%)Imbalance
R_gastro_catering is highly imbalanced (64.4%)Imbalance
R_grund_nonuse_1 is highly imbalanced (89.6%)Imbalance
R_grund_nonuse_2 is highly imbalanced (89.9%)Imbalance
R_grund_nonuse_3 is highly imbalanced (89.9%)Imbalance
R_grund_nonuse_4 is highly imbalanced (89.3%)Imbalance
R_grund_nonuse_5 is highly imbalanced (88.8%)Imbalance
R_grund_nonuse_5txt is highly imbalanced (98.3%)Imbalance
R_grund_nonuse_6 is highly imbalanced (89.0%)Imbalance
R_nutzung_retour is highly imbalanced (86.8%)Imbalance
R_nutzung_tk is highly imbalanced (86.0%)Imbalance
R_Park is highly imbalanced (99.4%)Imbalance
R_platz_andere is highly imbalanced (83.7%)Imbalance
R_platz_andere_txt is highly imbalanced (98.7%)Imbalance
R_platz_gebauede is highly imbalanced (83.8%)Imbalance
R_platz_perron_eng is highly imbalanced (83.0%)Imbalance
R_platz_perron_leute is highly imbalanced (83.0%)Imbalance
R_platz_unterf_eng is highly imbalanced (83.4%)Imbalance
R_platz_unterf_leute is highly imbalanced (82.9%)Imbalance
R_platz_vorbhf is highly imbalanced (83.8%)Imbalance
R_platz_warte is highly imbalanced (84.1%)Imbalance
R_sauber_anderes is highly imbalanced (87.3%)Imbalance
R_sauber_anderes_txt is highly imbalanced (99.4%)Imbalance
R_sauber_gebauede is highly imbalanced (86.6%)Imbalance
R_sauber_perron is highly imbalanced (86.4%)Imbalance
R_sauber_unterfuehrung is highly imbalanced (86.4%)Imbalance
R_sauber_vorbhf is highly imbalanced (86.4%)Imbalance
R_sauber_warte is highly imbalanced (86.7%)Imbalance
R_sauber_WC is highly imbalanced (86.9%)Imbalance
R_Sharing is highly imbalanced (99.9%)Imbalance
R_stoerung is highly imbalanced (50.1%)Imbalance
R_umsteig_andere is highly imbalanced (83.1%)Imbalance
R_umsteig_andere_txt is highly imbalanced (97.9%)Imbalance
R_umsteig_park is highly imbalanced (84.6%)Imbalance
R_umsteig_trambus is highly imbalanced (83.5%)Imbalance
R_umsteig_zug is highly imbalanced (82.9%)Imbalance
R_unzuf_comfort_txt is highly imbalanced (95.1%)Imbalance
R_unzuf_fahrplan_txt is highly imbalanced (94.4%)Imbalance
R_unzuf_gastro_ambiente_txt is highly imbalanced (87.8%)Imbalance
R_unzuf_gastro_auswahl_txt is highly imbalanced (88.1%)Imbalance
R_unzuf_gastro_freundlich_txt is highly imbalanced (84.2%)Imbalance
R_unzuf_gastro_kompetenz_txt is highly imbalanced (85.6%)Imbalance
R_unzuf_gastro_preis_txt is highly imbalanced (88.9%)Imbalance
R_unzuf_info_txt is highly imbalanced (97.4%)Imbalance
R_unzuf_mobile_txt is highly imbalanced (95.1%)Imbalance
R_unzuf_platzangebot_txt is highly imbalanced (93.4%)Imbalance
R_unzuf_preis_txt is highly imbalanced (89.1%)Imbalance
R_unzuf_gastro_quality_txt is highly imbalanced (86.6%)Imbalance
R_unzuf_puenktlichkeit_txt is highly imbalanced (95.6%)Imbalance
R_unzuf_Sauberkeit_Bhf_txt is highly imbalanced (96.2%)Imbalance
R_unzuf_sicherheit_zug is highly imbalanced (99.9%)Imbalance
R_unzuf_stoerungsinfo_txt is highly imbalanced (97.9%)Imbalance
R_unzuf_wc_avail_txt is highly imbalanced (98.0%)Imbalance
R_unzuf_wc_clean_txt is highly imbalanced (98.5%)Imbalance
R_unzuf_Wegweisung_Bhf_txt is highly imbalanced (96.4%)Imbalance
R_unzuf_zug_clean_txt is highly imbalanced (96.6%)Imbalance
R_unzuf_zugpers_txt is highly imbalanced (98.9%)Imbalance
R_Velo is highly imbalanced (98.8%)Imbalance
R_WC is highly imbalanced (99.7%)Imbalance
R_wc_nutzung is highly imbalanced (54.5%)Imbalance
R_weg_andere is highly imbalanced (88.4%)Imbalance
R_weg_andere_txt is highly imbalanced (98.7%)Imbalance
R_weg_laeden is highly imbalanced (88.9%)Imbalance
R_weg_park is highly imbalanced (89.2%)Imbalance
R_weg_share is highly imbalanced (89.3%)Imbalance
R_weg_trambus is highly imbalanced (88.3%)Imbalance
R_weg_velo is highly imbalanced (89.2%)Imbalance
R_weg_WC is highly imbalanced (88.6%)Imbalance
R_weg_zug is highly imbalanced (88.1%)Imbalance
RF_bhf_abfahrt is highly imbalanced (71.1%)Imbalance
RF_bhf_andere is highly imbalanced (74.9%)Imbalance
RF_bhf_perron is highly imbalanced (72.5%)Imbalance
RF_bhf_touch is highly imbalanced (74.9%)Imbalance
RF_kanal_14 is highly imbalanced (51.3%)Imbalance
RF_kanal_4 is highly imbalanced (64.1%)Imbalance
RF_kanal_6 is highly imbalanced (52.8%)Imbalance
RF_kanal_other is highly imbalanced (51.7%)Imbalance
RF_kanal_other_txt is highly imbalanced (98.1%)Imbalance
RF_mob_andere_txt is highly imbalanced (98.7%)Imbalance
RF_webs_andere is highly imbalanced (88.3%)Imbalance
RF_webs_andere_txt is highly imbalanced (99.8%)Imbalance
RF_webs_erwsuche is highly imbalanced (87.7%)Imbalance
RF_webs_fahrplan is highly imbalanced (88.0%)Imbalance
RF_webs_karte is highly imbalanced (88.1%)Imbalance
RF_zufallsitem1_label is highly imbalanced (61.1%)Imbalance
S_AB1_GA2kl is highly imbalanced (61.9%)Imbalance
S_AB2_GA is highly imbalanced (53.6%)Imbalance
S_AB4 is highly imbalanced (64.7%)Imbalance
S_AB5 is highly imbalanced (51.8%)Imbalance
S_AB6 is highly imbalanced (72.4%)Imbalance
S_AB7 is highly imbalanced (66.0%)Imbalance
S_AB7txt is highly imbalanced (90.0%)Imbalance
S_AB8 is highly imbalanced (51.5%)Imbalance
S_sex is highly imbalanced (52.0%)Imbalance
S_sprache is highly imbalanced (53.3%)Imbalance
S_wohnsitz is highly imbalanced (80.7%)Imbalance
SF_kanal_1_zuf is highly imbalanced (95.1%)Imbalance
SF_kanal_12_zuf is highly imbalanced (91.0%)Imbalance
SF_kanal_13_zuf is highly imbalanced (98.8%)Imbalance
SF_kanal_14_zuf is highly imbalanced (98.1%)Imbalance
SF_kanal_2_zuf is highly imbalanced (99.4%)Imbalance
SF_kanal_4_zuf is highly imbalanced (99.2%)Imbalance
SF_kanal_6_zuf is highly imbalanced (99.4%)Imbalance
SF_kanal_7_zuf is highly imbalanced (93.8%)Imbalance
SF_kanal_8_zuf is highly imbalanced (93.4%)Imbalance
SF_kanal_other_zuf is highly imbalanced (98.2%)Imbalance
RF_kanal_8_Zuf is highly imbalanced (51.4%)Imbalance
RF_kanal_13_Zuf is highly imbalanced (51.5%)Imbalance
RF_kanal_14_Zuf is highly imbalanced (50.7%)Imbalance
SF_bhf_abfahrt is highly imbalanced (94.3%)Imbalance
SF_bhf_andere is highly imbalanced (94.7%)Imbalance
SF_bhf_perron is highly imbalanced (94.4%)Imbalance
SF_bhf_stoerung is highly imbalanced (94.6%)Imbalance
SF_bhf_touch is highly imbalanced (94.9%)Imbalance
SF_info_art_1 is highly imbalanced (74.9%)Imbalance
SF_info_art_2 is highly imbalanced (74.9%)Imbalance
SF_info_art_3 is highly imbalanced (76.2%)Imbalance
SF_info_art_4 is highly imbalanced (99.7%)Imbalance
SF_info_art_5 is highly imbalanced (75.1%)Imbalance
SF_info_art_6 is highly imbalanced (74.9%)Imbalance
SF_info_art_7 is highly imbalanced (83.9%)Imbalance
SF_kanal_1 is highly imbalanced (72.2%)Imbalance
SF_kanal_12 is highly imbalanced (71.3%)Imbalance
SF_kanal_13 is highly imbalanced (74.7%)Imbalance
SF_kanal_14 is highly imbalanced (74.2%)Imbalance
SF_kanal_15 is highly imbalanced (77.1%)Imbalance
SF_kanal_2 is highly imbalanced (75.3%)Imbalance
SF_kanal_4 is highly imbalanced (74.4%)Imbalance
SF_kanal_6 is highly imbalanced (75.2%)Imbalance
SF_kanal_7 is highly imbalanced (71.9%)Imbalance
SF_kanal_8 is highly imbalanced (71.8%)Imbalance
SF_kanal_keiner is highly imbalanced (74.1%)Imbalance
SF_kanal_other is highly imbalanced (74.2%)Imbalance
SF_kanal_other_txt is highly imbalanced (97.9%)Imbalance
SF_mob_andere is highly imbalanced (94.1%)Imbalance
SF_mob_andere_txt is highly imbalanced (99.9%)Imbalance
SF_mob_autom is highly imbalanced (93.6%)Imbalance
SF_mob_fahrplan is highly imbalanced (93.9%)Imbalance
SF_mob_karte is highly imbalanced (94.1%)Imbalance
u_artikel is highly imbalanced (56.0%)Imbalance
SF_unzuf_info_txt is highly imbalanced (98.7%)Imbalance
SF_Zufallsitem1 is highly imbalanced (86.4%)Imbalance
SF_Zufallsitem2 is highly imbalanced (88.5%)Imbalance
u_fahrausweis is highly imbalanced (58.7%)Imbalance
u_ga is highly imbalanced (57.1%)Imbalance
u_kategorie is highly imbalanced (61.9%)Imbalance
u_klassencode is highly imbalanced (66.6%)Imbalance
u_ticket is highly imbalanced (50.2%)Imbalance
u_zusatz is highly imbalanced (52.3%)Imbalance
wime_stoerungsinfo is highly imbalanced (80.5%)Imbalance
wime_unzuf_sf_txt is highly imbalanced (98.4%)Imbalance
SF_kanal_zuf has 367364 (67.2%) missing valuesMissing
wime_kundenorientierung has 367364 (67.2%) missing valuesMissing
wime_mobile has 367364 (67.2%) missing valuesMissing
Wime_Sauberkeit_BhfZiel has 328381 (60.1%) missing valuesMissing
Wime_Sauberkeit_BhfStart has 328381 (60.1%) missing valuesMissing
wime_sf_behebung has 367364 (67.2%) missing valuesMissing
Wime_Wegweisung_BhfZiel has 328381 (60.1%) missing valuesMissing
Wime_Wegweisung_BhfStart has 328381 (60.1%) missing valuesMissing
BFH_sahre_non has 437164 (80.0%) missing valuesMissing
BFH_sahre_start has 437164 (80.0%) missing valuesMissing
BFH_sahre_ziel has 437164 (80.0%) missing valuesMissing
BHF_park has 437162 (80.0%) missing valuesMissing
BHF_park_non has 437164 (80.0%) missing valuesMissing
BHF_park_start has 437164 (80.0%) missing valuesMissing
BHF_park_ziel has 437164 (80.0%) missing valuesMissing
BHF_platz has 437162 (80.0%) missing valuesMissing
BHF_sauberkeit has 437162 (80.0%) missing valuesMissing
BHF_share has 437162 (80.0%) missing valuesMissing
BHF_umsteig has 437162 (80.0%) missing valuesMissing
BHF_velo has 437162 (80.0%) missing valuesMissing
BHF_velo_non has 437164 (80.0%) missing valuesMissing
BHF_velo_start has 437164 (80.0%) missing valuesMissing
BHF_velo_ziel has 437164 (80.0%) missing valuesMissing
BHF_wc has 437162 (80.0%) missing valuesMissing
BHF_wegweisung has 437162 (80.0%) missing valuesMissing
date_of_first_mail has 367358 (67.2%) missing valuesMissing
date_of_last_access has 165697 (30.3%) missing valuesMissing
device_type has 328380 (60.1%) missing valuesMissing
dispcode has 328380 (60.1%) missing valuesMissing
EKL_fehlende_infos_txt has 219109 (40.1%) missing valuesMissing
EKL_info_nutzung has 218263 (39.9%) missing valuesMissing
EKL_info_vermisst has 218263 (39.9%) missing valuesMissing
EKL_info_zielerreichung has 218263 (39.9%) missing valuesMissing
EKL_reiseaenderung has 218263 (39.9%) missing valuesMissing
OES_zuf_personal_angemessen has 109481 (20.0%) missing valuesMissing
OES_zuf_personal_aufmerksam has 109481 (20.0%) missing valuesMissing
OES_zuf_personal_effekt has 109481 (20.0%) missing valuesMissing
Wime_Platz_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Umsteig_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Sicherheit_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_WC_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Velo_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Park_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Share_BhfStart has 437164 (80.0%) missing valuesMissing
Wime_Platz_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_Umsteig_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_Sicherheit_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_WC_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_Velo_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_Park_BhfZiel has 437164 (80.0%) missing valuesMissing
Wime_Share_BhfZiel has 437164 (80.0%) missing valuesMissing
wime_sicherheitskraft has 437164 (80.0%) missing valuesMissing
fg_abfahrt has 41207 (7.5%) missing valuesMissing
fg_ankunft has 41209 (7.5%) missing valuesMissing
ft_abfahrt has 55368 (10.1%) missing valuesMissing
ft_ankunft has 55368 (10.1%) missing valuesMissing
ft_haltestellen has 165697 (30.3%) missing valuesMissing
ft_tu has 165697 (30.3%) missing valuesMissing
ft_uic_haltestellen has 165697 (30.3%) missing valuesMissing
OES_beeintraechtigung has 218263 (39.9%) missing valuesMissing
OES_grund_beeintraecht_2 has 218263 (39.9%) missing valuesMissing
OES_grund_beeintraecht_5 has 328381 (60.1%) missing valuesMissing
OES_grund_beeintraecht_6 has 328381 (60.1%) missing valuesMissing
OES_grund_beeintraecht_7 has 437164 (80.0%) missing valuesMissing
OES_grund_beeintraecht_7_txt has 437164 (80.0%) missing valuesMissing
OES_grund_personal_negativ_txt has 109481 (20.0%) missing valuesMissing
OES_personal_bhf_ziel has 437164 (80.0%) missing valuesMissing
OES_personal_perron_ziel has 437164 (80.0%) missing valuesMissing
OES_personal_wunsch has 437164 (80.0%) missing valuesMissing
Ortskundigkeit has 437164 (80.0%) missing valuesMissing
R_abo_datum has 367363 (67.2%) missing valuesMissing
R_abo_nutzung has 367363 (67.2%) missing valuesMissing
R_abotk_klasse has 367363 (67.2%) missing valuesMissing
R_anschluss_1 has 367363 (67.2%) missing valuesMissing
R_anschluss_2 has 367363 (67.2%) missing valuesMissing
R_anschluss_3 has 367363 (67.2%) missing valuesMissing
R_grund_nonuse_1 has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_2 has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_3 has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_4 has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_5 has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_5txt has 328381 (60.1%) missing valuesMissing
R_grund_nonuse_6 has 328381 (60.1%) missing valuesMissing
R_kb_wunsch has 367363 (67.2%) missing valuesMissing
R_nutzung_einfach has 328380 (60.1%) missing valuesMissing
R_nutzung_retour has 328380 (60.1%) missing valuesMissing
R_nutzung_tk has 367363 (67.2%) missing valuesMissing
R_Park has 437164 (80.0%) missing valuesMissing
R_platz_andere has 437164 (80.0%) missing valuesMissing
R_platz_andere_txt has 437164 (80.0%) missing valuesMissing
R_platz_gebauede has 437164 (80.0%) missing valuesMissing
R_platz_perron_eng has 437164 (80.0%) missing valuesMissing
R_platz_perron_leute has 437164 (80.0%) missing valuesMissing
R_platz_unterf_eng has 437164 (80.0%) missing valuesMissing
R_platz_unterf_leute has 437164 (80.0%) missing valuesMissing
R_platz_vorbhf has 437164 (80.0%) missing valuesMissing
R_platz_warte has 437164 (80.0%) missing valuesMissing
R_sauber_anderes has 437164 (80.0%) missing valuesMissing
R_sauber_anderes_txt has 437164 (80.0%) missing valuesMissing
R_sauber_gebauede has 437164 (80.0%) missing valuesMissing
R_sauber_perron has 437164 (80.0%) missing valuesMissing
R_sauber_unterfuehrung has 437164 (80.0%) missing valuesMissing
R_sauber_vorbhf has 437164 (80.0%) missing valuesMissing
R_sauber_warte has 437164 (80.0%) missing valuesMissing
R_sauber_WC has 437164 (80.0%) missing valuesMissing
R_Sharing has 437164 (80.0%) missing valuesMissing
R_umsteig_andere has 437164 (80.0%) missing valuesMissing
R_umsteig_andere_txt has 437164 (80.0%) missing valuesMissing
R_umsteig_park has 437164 (80.0%) missing valuesMissing
R_umsteig_trambus has 437164 (80.0%) missing valuesMissing
R_umsteig_zug has 437164 (80.0%) missing valuesMissing
R_unzuf_comfort_txt has 11957 (2.2%) missing valuesMissing
R_unzuf_fahrplan_txt has 367364 (67.2%) missing valuesMissing
R_unzuf_info_txt has 328381 (60.1%) missing valuesMissing
R_unzuf_mobile_txt has 367364 (67.2%) missing valuesMissing
R_unzuf_platzangebot_txt has 328381 (60.1%) missing valuesMissing
R_unzuf_preis_txt has 367364 (67.2%) missing valuesMissing
R_unzuf_puenktlichkeit_txt has 328381 (60.1%) missing valuesMissing
R_unzuf_Sauberkeit_Bhf_txt has 437861 (80.1%) missing valuesMissing
R_unzuf_sicherheit_zug has 437164 (80.0%) missing valuesMissing
R_unzuf_stoerungsinfo_txt has 218263 (39.9%) missing valuesMissing
R_unzuf_Wegweisung_Bhf_txt has 437861 (80.1%) missing valuesMissing
R_Velo has 437164 (80.0%) missing valuesMissing
R_WC has 437164 (80.0%) missing valuesMissing
R_wc_na_start has 437164 (80.0%) missing valuesMissing
R_wc_na_ziel has 437164 (80.0%) missing valuesMissing
R_wc_na_zug has 437164 (80.0%) missing valuesMissing
R_wc_start has 437164 (80.0%) missing valuesMissing
R_wc_ziel has 437164 (80.0%) missing valuesMissing
R_wc_zug has 437164 (80.0%) missing valuesMissing
R_weg_andere has 437164 (80.0%) missing valuesMissing
R_weg_andere_txt has 437164 (80.0%) missing valuesMissing
R_weg_laeden has 437164 (80.0%) missing valuesMissing
R_weg_park has 437164 (80.0%) missing valuesMissing
R_weg_share has 437164 (80.0%) missing valuesMissing
R_weg_trambus has 437164 (80.0%) missing valuesMissing
R_weg_velo has 437164 (80.0%) missing valuesMissing
R_weg_WC has 437164 (80.0%) missing valuesMissing
R_weg_zug has 437164 (80.0%) missing valuesMissing
RF_bhf_abfahrt has 437164 (80.0%) missing valuesMissing
RF_bhf_andere has 437164 (80.0%) missing valuesMissing
RF_bhf_perron has 437164 (80.0%) missing valuesMissing
RF_bhf_touch has 437164 (80.0%) missing valuesMissing
RF_kanal_1 has 299312 (54.8%) missing valuesMissing
RF_kanal_12 has 299312 (54.8%) missing valuesMissing
RF_kanal_13 has 299312 (54.8%) missing valuesMissing
RF_kanal_14 has 299312 (54.8%) missing valuesMissing
RF_kanal_15 has 437164 (80.0%) missing valuesMissing
RF_kanal_2 has 299312 (54.8%) missing valuesMissing
RF_kanal_4 has 408792 (74.8%) missing valuesMissing
RF_kanal_6 has 299312 (54.8%) missing valuesMissing
RF_kanal_7 has 299312 (54.8%) missing valuesMissing
RF_kanal_8 has 299312 (54.8%) missing valuesMissing
RF_kanal_keiner has 299312 (54.8%) missing valuesMissing
RF_kanal_other has 299312 (54.8%) missing valuesMissing
RF_kanal_other_txt has 299312 (54.8%) missing valuesMissing
RF_mob_andere has 437164 (80.0%) missing valuesMissing
RF_mob_andere_txt has 437164 (80.0%) missing valuesMissing
RF_mob_autom has 437164 (80.0%) missing valuesMissing
RF_mob_fahrplan has 437164 (80.0%) missing valuesMissing
RF_mob_karte has 437164 (80.0%) missing valuesMissing
RF_webs_andere has 437164 (80.0%) missing valuesMissing
RF_webs_andere_txt has 437164 (80.0%) missing valuesMissing
RF_webs_erwsuche has 437164 (80.0%) missing valuesMissing
RF_webs_fahrplan has 437164 (80.0%) missing valuesMissing
RF_webs_karte has 437164 (80.0%) missing valuesMissing
RF_Zufallsitem1 has 227748 (41.7%) missing valuesMissing
RF_zufallsitem1_label has 301740 (55.2%) missing valuesMissing
S_AB1_GA2kl has 218263 (39.9%) missing valuesMissing
S_AB2_GA has 328380 (60.1%) missing valuesMissing
S_AB2_GA1kl has 218263 (39.9%) missing valuesMissing
S_berufstaetigkeit has 367363 (67.2%) missing valuesMissing
S_Usertyp1 has 367363 (67.2%) missing valuesMissing
S_Usertyp2 has 367363 (67.2%) missing valuesMissing
S_Usertyp3 has 367363 (67.2%) missing valuesMissing
SF_kanal_1_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_12_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_13_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_14_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_2_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_4_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_6_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_7_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_8_zuf has 179285 (32.8%) missing valuesMissing
SF_kanal_other_zuf has 179285 (32.8%) missing valuesMissing
RF_kanal_1_Zuf has 463585 (84.8%) missing valuesMissing
RF_kanal_2_Zuf has 537352 (98.3%) missing valuesMissing
RF_kanal_4_Zuf has 543761 (99.5%) missing valuesMissing
RF_kanal_6_Zuf has 545920 (99.9%) missing valuesMissing
RF_kanal_7_Zuf has 541656 (99.1%) missing valuesMissing
RF_kanal_8_Zuf has 528956 (96.8%) missing valuesMissing
RF_kanal_12_Zuf has 537123 (98.3%) missing valuesMissing
RF_kanal_13_Zuf has 535744 (98.0%) missing valuesMissing
RF_kanal_14_Zuf has 543242 (99.4%) missing valuesMissing
RF_kanal_24_Zuf has 546643 (100.0%) missing valuesMissing
SF_bhf_abfahrt has 437164 (80.0%) missing valuesMissing
SF_bhf_andere has 437164 (80.0%) missing valuesMissing
SF_bhf_perron has 437164 (80.0%) missing valuesMissing
SF_bhf_stoerung has 437164 (80.0%) missing valuesMissing
SF_bhf_touch has 437164 (80.0%) missing valuesMissing
SF_info_art_4 has 179285 (32.8%) missing valuesMissing
SF_kanal_15 has 437164 (80.0%) missing valuesMissing
SF_kanal_4 has 109481 (20.0%) missing valuesMissing
SF_mob_andere has 437164 (80.0%) missing valuesMissing
SF_mob_andere_txt has 437164 (80.0%) missing valuesMissing
SF_mob_autom has 437164 (80.0%) missing valuesMissing
SF_mob_fahrplan has 437164 (80.0%) missing valuesMissing
SF_mob_karte has 437164 (80.0%) missing valuesMissing
u_artikel has 165697 (30.3%) missing valuesMissing
SF_unzuf_info_txt has 328381 (60.1%) missing valuesMissing
SF_Zufallsitem1 has 367358 (67.2%) missing valuesMissing
SF_Zufallsitem2 has 367358 (67.2%) missing valuesMissing
u_fahrausweis has 367363 (67.2%) missing valuesMissing
u_ga has 218263 (39.9%) missing valuesMissing
u_kategorie has 379465 (69.4%) missing valuesMissing
u_kaufdatum has 50958 (9.3%) missing valuesMissing
u_zusatz has 54952 (10.1%) missing valuesMissing
wime_stoerungsinfo has 218263 (39.9%) missing valuesMissing
wime_unzuf_sf_txt has 367364 (67.2%) missing valuesMissing
participant has unique valuesUnique
participant_id has unique valuesUnique
file_name is an unsupported type, check if it needs cleaning or further analysisUnsupported
RF_kanal_24_Zuf is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-01-04 16:46:44.772362
Analysis finished2023-01-04 16:47:41.272608
Duration56.5 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

time
Date

Distinct2111
Distinct (%)0.4%
Missing96
Missing (%)< 0.1%
Memory size4.2 MiB
Minimum2017-01-01 00:00:00
Maximum2023-01-03 00:00:00
2023-01-04T17:47:41.449833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T17:47:41.613718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

participant
Real number (ℝ)

Distinct546643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311748.3125
Minimum1
Maximum604555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-01-04T17:47:41.913424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27333.1
Q1181466.5
median318127
Q3465237.5
95-th percentile576169.9
Maximum604555
Range604554
Interquartile range (IQR)283771

Descriptive statistics

Standard deviation175943.447
Coefficient of variation (CV)0.5643765817
Kurtosis-1.150042744
Mean311748.3125
Median Absolute Deviation (MAD)141886
Skewness-0.1245952288
Sum1.704150328 × 1011
Variance3.095609653 × 1010
MonotonicityStrictly increasing
2023-01-04T17:47:42.053313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
410231 1
 
< 0.1%
410245 1
 
< 0.1%
410244 1
 
< 0.1%
410243 1
 
< 0.1%
410242 1
 
< 0.1%
410241 1
 
< 0.1%
410240 1
 
< 0.1%
410239 1
 
< 0.1%
410238 1
 
< 0.1%
Other values (546633) 546633
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
604555 1
< 0.1%
604554 1
< 0.1%
604553 1
< 0.1%
604552 1
< 0.1%
604551 1
< 0.1%

UmfrageName
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
kuzu_zug
546643 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkuzu_zug
2nd rowkuzu_zug
3rd rowkuzu_zug
4th rowkuzu_zug
5th rowkuzu_zug

Common Values

ValueCountFrequency (%)
kuzu_zug 546643
100.0%

Common Values (Plot)

2023-01-04T17:47:42.197747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

file_name
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size4.2 MiB

wime_puenktlich
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
201959 
5
119570 
-77
66804 
9
46850 
4
30524 
Other values (7)
80935 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row10

Common Values

ValueCountFrequency (%)
10 201959
36.9%
5 119570
21.9%
-77 66804
 
12.2%
9 46850
 
8.6%
4 30524
 
5.6%
8 29246
 
5.4%
1 12532
 
2.3%
7 12100
 
2.2%
3 10631
 
1.9%
2 6271
 
1.1%
Other values (2) 10155
 
1.9%

wime_komfort
Categorical

Distinct12
Distinct (%)< 0.1%
Missing4182
Missing (%)0.8%
Memory size4.2 MiB
5
89455 
10
88437 
8
70001 
-77
65667 
4
62150 
Other values (7)
166751 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row10

Common Values

ValueCountFrequency (%)
5 89455
16.4%
10 88437
16.2%
8 70001
12.8%
-77 65667
12.0%
4 62150
11.4%
9 53248
9.7%
7 42572
7.8%
6 23275
 
4.3%
3 22500
 
4.1%
2 8444
 
1.5%
Other values (2) 16712
 
3.1%

wime_fahrplan
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
5
148842 
10
118894 
8
59631 
4
55199 
9
48898 
Other values (7)
115178 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row6
3rd row10
4th row4
5th row10

Common Values

ValueCountFrequency (%)
5 148842
27.2%
10 118894
21.7%
8 59631
10.9%
4 55199
 
10.1%
9 48898
 
8.9%
7 32886
 
6.0%
3 23030
 
4.2%
6 17548
 
3.2%
1 12910
 
2.4%
2 10813
 
2.0%
Other values (2) 17991
 
3.3%

SF_kanal_zuf
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-77
169425 
5
 
3437
4
 
2848
3
 
1804
2
 
839
Other values (2)
 
926

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 169425
31.0%
5 3437
 
0.6%
4 2848
 
0.5%
3 1804
 
0.3%
2 839
 
0.2%
1 703
 
0.1%
weiss nicht 223
 
< 0.1%
(Missing) 367364
67.2%

Common Values (Plot)

2023-01-04T17:47:42.322775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

wime_infokanal
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
244538 
5
107509 
10
69202 
4
37284 
9
26229 
Other values (8)
61880 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
-77 244538
44.7%
5 107509
19.7%
10 69202
 
12.7%
4 37284
 
6.8%
9 26229
 
4.8%
8 23366
 
4.3%
7 9975
 
1.8%
3 9114
 
1.7%
weiss nicht 7950
 
1.5%
6 4520
 
0.8%
Other values (3) 6955
 
1.3%

wime_personal
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
267399 
10
89346 
weiss nicht
53112 
5
52477 
9
 
25707
Other values (7)
58601 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row10
4th row10
5th row-77

Common Values

ValueCountFrequency (%)
-77 267399
48.9%
10 89346
 
16.3%
weiss nicht 53112
 
9.7%
5 52477
 
9.6%
9 25707
 
4.7%
8 23164
 
4.2%
4 15332
 
2.8%
7 8937
 
1.6%
6 3870
 
0.7%
3 3821
 
0.7%
Other values (2) 3477
 
0.6%
Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-77
71541 
5
44310 
4
40624 
3
11422 
weiss nicht
 
6933
Other values (2)
 
4449

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row5
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 71541
 
13.1%
5 44310
 
8.1%
4 40624
 
7.4%
3 11422
 
2.1%
weiss nicht 6933
 
1.3%
2 2964
 
0.5%
1 1485
 
0.3%
(Missing) 367364
67.2%

Common Values (Plot)

2023-01-04T17:47:42.466518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

wime_mobile
Categorical

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-77
51691 
5
43489 
4
31702 
weiss nicht
31288 
3
12867 
Other values (2)
8242 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row5
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 51691
 
9.5%
5 43489
 
8.0%
4 31702
 
5.8%
weiss nicht 31288
 
5.7%
3 12867
 
2.4%
2 5224
 
1.0%
1 3018
 
0.6%
(Missing) 367364
67.2%

Common Values (Plot)

2023-01-04T17:47:42.611509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
134433 
5
104883 
-77
66804 
8
49803 
4
47723 
Other values (7)
142996 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row6
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 134433
24.6%
5 104883
19.2%
-77 66804
12.2%
8 49803
 
9.1%
4 47723
 
8.7%
9 42398
 
7.8%
7 28634
 
5.2%
3 24022
 
4.4%
6 16576
 
3.0%
1 16340
 
3.0%
Other values (2) 15026
 
2.7%
Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
5
110090 
4
79123 
10
73110 
8
52899 
3
50640 
Other values (7)
180780 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row4
3rd row10
4th row7
5th row10

Common Values

ValueCountFrequency (%)
5 110090
20.1%
4 79123
14.5%
10 73110
13.4%
8 52899
9.7%
3 50640
9.3%
7 43074
 
7.9%
-77 38811
 
7.1%
6 28542
 
5.2%
9 28345
 
5.2%
2 19938
 
3.6%
Other values (2) 22070
 
4.0%
Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
4
68629 
5
62724 
-77
52606 
3
20592 
weiss nicht
9392 
Other values (2)
 
4319

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row5
4th rowweiss nicht
5th row5

Common Values

ValueCountFrequency (%)
4 68629
 
12.6%
5 62724
 
11.5%
-77 52606
 
9.6%
3 20592
 
3.8%
weiss nicht 9392
 
1.7%
2 3403
 
0.6%
1 916
 
0.2%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:42.770300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
4
69235 
5
62965 
-77
52606 
3
20666 
weiss nicht
8145 
Other values (2)
 
4645

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 69235
 
12.7%
5 62965
 
11.5%
-77 52606
 
9.6%
3 20666
 
3.8%
weiss nicht 8145
 
1.5%
2 3592
 
0.7%
1 1053
 
0.2%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:42.936959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

wime_sauberkeit
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
91319 
5
87646 
8
72496 
-77
66804 
4
65981 
Other values (7)
162396 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row9
5th row5

Common Values

ValueCountFrequency (%)
10 91319
16.7%
5 87646
16.0%
8 72496
13.3%
-77 66804
12.2%
4 65981
12.1%
9 56524
10.3%
7 42740
7.8%
3 24469
 
4.5%
6 21813
 
4.0%
2 7611
 
1.4%
Other values (2) 9239
 
1.7%

wime_wc
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
482817 
5
 
11087
4
 
10603
8
 
7762
10
 
7676
Other values (7)
 
26697

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 482817
88.3%
5 11087
 
2.0%
4 10603
 
1.9%
8 7762
 
1.4%
10 7676
 
1.4%
3 6044
 
1.1%
7 5709
 
1.0%
9 5172
 
0.9%
6 3825
 
0.7%
2 2637
 
0.5%
Other values (2) 3310
 
0.6%

wime_oes_ziel
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
135140 
5
88728 
-77
79375 
9
67427 
8
60114 
Other values (8)
115858 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 135140
24.7%
5 88728
16.2%
-77 79375
14.5%
9 67427
12.3%
8 60114
11.0%
4 48066
 
8.8%
7 28952
 
5.3%
3 14465
 
2.6%
6 13569
 
2.5%
weiss nicht 4802
 
0.9%
Other values (3) 6004
 
1.1%

wime_oes_start
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
150932 
5
95593 
-77
81620 
9
63632 
8
55703 
Other values (7)
99162 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 150932
27.6%
5 95593
17.5%
-77 81620
14.9%
9 63632
11.6%
8 55703
 
10.2%
4 43200
 
7.9%
7 24889
 
4.6%
3 11428
 
2.1%
6 10525
 
1.9%
weiss nicht 3926
 
0.7%
Other values (2) 5194
 
1.0%

wime_oes_fahrt
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
168056 
5
102652 
-77
81620 
9
70396 
8
51206 
Other values (8)
72712 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 168056
30.7%
5 102652
18.8%
-77 81620
14.9%
9 70396
12.9%
8 51206
 
9.4%
4 37362
 
6.8%
7 17387
 
3.2%
3 6458
 
1.2%
6 6313
 
1.2%
weiss nicht 1933
 
0.4%
Other values (3) 3259
 
0.6%

wime_sf_behebung
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-77
168354 
5
 
2700
4
 
2672
3
 
2125
1
 
1291
Other values (2)
 
2137

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 168354
30.8%
5 2700
 
0.5%
4 2672
 
0.5%
3 2125
 
0.4%
1 1291
 
0.2%
weiss nicht 1094
 
0.2%
2 1043
 
0.2%
(Missing) 367364
67.2%

Common Values (Plot)

2023-01-04T17:47:43.088585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

wime_wc_verfuegb
Categorical

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
473562 
5
 
13879
10
 
13494
4
 
9292
8
 
7556
Other values (7)
 
28859

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rowweiss nicht
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 473562
86.6%
5 13879
 
2.5%
10 13494
 
2.5%
4 9292
 
1.7%
8 7556
 
1.4%
9 6412
 
1.2%
1 5525
 
1.0%
3 4490
 
0.8%
7 4464
 
0.8%
weiss nicht 2749
 
0.5%
Other values (2) 5219
 
1.0%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
weiss nicht
 
5163
8
 
4381
10
 
3839
5
 
3683
Other values (8)
 
12210

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
weiss nicht 5163
 
0.9%
8 4381
 
0.8%
10 3839
 
0.7%
5 3683
 
0.7%
7 3022
 
0.6%
4 2872
 
0.5%
9 2710
 
0.5%
6 1799
 
0.3%
3 1117
 
0.2%
Other values (3) 690
 
0.1%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
weiss nicht
 
6598
8
 
3691
10
 
3680
5
 
3352
Other values (8)
 
11955

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
weiss nicht 6598
 
1.2%
8 3691
 
0.7%
10 3680
 
0.7%
5 3352
 
0.6%
4 2783
 
0.5%
7 2782
 
0.5%
9 2313
 
0.4%
6 1723
 
0.3%
3 1350
 
0.2%
Other values (3) 1004
 
0.2%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
10
 
7844
weiss nicht
 
5690
5
 
4036
9
 
3443
Other values (8)
 
8263

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
10 7844
 
1.4%
weiss nicht 5690
 
1.0%
5 4036
 
0.7%
9 3443
 
0.6%
8 3386
 
0.6%
7 1588
 
0.3%
4 1484
 
0.3%
6 812
 
0.1%
3 476
 
0.1%
Other values (3) 517
 
0.1%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
weiss nicht
 
6934
10
 
6386
5
 
3606
8
 
3529
Other values (8)
 
8821

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
weiss nicht 6934
 
1.3%
10 6386
 
1.2%
5 3606
 
0.7%
8 3529
 
0.6%
9 3102
 
0.6%
7 1843
 
0.3%
4 1824
 
0.3%
6 928
 
0.2%
3 569
 
0.1%
Other values (3) 555
 
0.1%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
weiss nicht
 
5477
5
 
3618
4
 
3556
8
 
3407
Other values (8)
 
13218

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
weiss nicht 5477
 
1.0%
5 3618
 
0.7%
4 3556
 
0.7%
8 3407
 
0.6%
7 2975
 
0.5%
10 2362
 
0.4%
6 2347
 
0.4%
3 2083
 
0.4%
9 1823
 
0.3%
Other values (3) 1628
 
0.3%
Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
517366 
weiss nicht
 
5815
10
 
4544
8
 
4143
5
 
3536
Other values (8)
 
11238

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 517366
94.6%
weiss nicht 5815
 
1.1%
10 4544
 
0.8%
8 4143
 
0.8%
5 3536
 
0.6%
9 2919
 
0.5%
7 2614
 
0.5%
4 2605
 
0.5%
6 1480
 
0.3%
3 935
 
0.2%
Other values (3) 685
 
0.1%
Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
5
81443 
4
57607 
-77
52606 
3
14457 
weiss nicht
 
8086
Other values (2)
 
4063

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 81443
 
14.9%
4 57607
 
10.5%
-77 52606
 
9.6%
3 14457
 
2.6%
weiss nicht 8086
 
1.5%
2 3001
 
0.5%
1 1062
 
0.2%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:43.241236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
5
86646 
4
56079 
-77
52606 
3
12562 
weiss nicht
 
6827
Other values (2)
 
3542

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 86646
 
15.9%
4 56079
 
10.3%
-77 52606
 
9.6%
3 12562
 
2.3%
weiss nicht 6827
 
1.2%
2 2598
 
0.5%
1 944
 
0.2%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:43.391014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BFH_sahre_non
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
quoted
64858 
-77
44193 
not quoted
 
428

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 64858
 
11.9%
-77 44193
 
8.1%
not quoted 428
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:43.523567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BFH_sahre_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
54931 
not quoted
54302 
quoted
 
246

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 54931
 
10.0%
not quoted 54302
 
9.9%
quoted 246
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:43.629471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BFH_sahre_ziel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
66557 
not quoted
42716 
quoted
 
206

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 66557
 
12.2%
not quoted 42716
 
7.8%
quoted 206
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:43.734233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_park
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct145
Distinct (%)0.1%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
109124 
Sion
 
17
Olten
 
16
Bern
 
11
Lugano
 
10
Other values (140)
 
303

Unique

Unique87 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109124
 
20.0%
Sion 17
 
< 0.1%
Olten 16
 
< 0.1%
Bern 11
 
< 0.1%
Lugano 10
 
< 0.1%
Täsch 9
 
< 0.1%
Coppet 9
 
< 0.1%
Nyon 9
 
< 0.1%
Neuchâtel 8
 
< 0.1%
Visp 8
 
< 0.1%
Other values (135) 260
 
< 0.1%
(Missing) 437162
80.0%

BHF_park_non
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
quoted
60959 
-77
44193 
not quoted
 
4327

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 60959
 
11.2%
-77 44193
 
8.1%
not quoted 4327
 
0.8%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:43.842339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_park_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
54931 
not quoted
51411 
quoted
 
3137

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 54931
 
10.0%
not quoted 51411
 
9.4%
quoted 3137
 
0.6%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:43.947194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_park_ziel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
66557 
not quoted
41715 
quoted
 
1207

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 66557
 
12.2%
not quoted 41715
 
7.6%
quoted 1207
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:44.052570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_platz
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct425
Distinct (%)0.4%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
105326 
Bern
 
704
Basel SBB
 
322
Lausanne
 
296
Zürich HB
 
247
Other values (420)
 
2586

Unique

Unique228 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 105326
 
19.3%
Bern 704
 
0.1%
Basel SBB 322
 
0.1%
Lausanne 296
 
0.1%
Zürich HB 247
 
< 0.1%
Luzern 209
 
< 0.1%
Olten 150
 
< 0.1%
Genève 132
 
< 0.1%
Liestal 90
 
< 0.1%
Biel/Bienne 75
 
< 0.1%
Other values (415) 1930
 
0.4%
(Missing) 437162
80.0%

BHF_sauberkeit
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct502
Distinct (%)0.5%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
106339 
Zürich HB
 
264
Bern
 
242
Lausanne
 
192
Genève
 
105
Other values (497)
 
2339

Unique

Unique266 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th rowRheinfelden

Common Values

ValueCountFrequency (%)
-66 106339
 
19.5%
Zürich HB 264
 
< 0.1%
Bern 242
 
< 0.1%
Lausanne 192
 
< 0.1%
Genève 105
 
< 0.1%
Olten 99
 
< 0.1%
Biel/Bienne 97
 
< 0.1%
Basel SBB 93
 
< 0.1%
Milano Centrale 77
 
< 0.1%
Luzern 55
 
< 0.1%
Other values (492) 1918
 
0.4%
(Missing) 437162
80.0%

BHF_share
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
109467 
Zürich HB
 
3
Neuchâtel
 
2
Dachsen
 
1
Fribourg/Freiburg
 
1
Other values (7)
 
7

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109467
 
20.0%
Zürich HB 3
 
< 0.1%
Neuchâtel 2
 
< 0.1%
Dachsen 1
 
< 0.1%
Fribourg/Freiburg 1
 
< 0.1%
Bellinzona 1
 
< 0.1%
Bern 1
 
< 0.1%
Interlaken Ost 1
 
< 0.1%
Ardon 1
 
< 0.1%
Genève 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 437162
80.0%

BHF_umsteig
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct555
Distinct (%)0.5%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
105289 
Bern
 
534
Zürich HB
 
394
Basel SBB
 
227
Luzern
 
177
Other values (550)
 
2860

Unique

Unique316 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 105289
 
19.3%
Bern 534
 
0.1%
Zürich HB 394
 
0.1%
Basel SBB 227
 
< 0.1%
Luzern 177
 
< 0.1%
Olten 162
 
< 0.1%
Lausanne 121
 
< 0.1%
Visp 73
 
< 0.1%
Genève 72
 
< 0.1%
Winterthur 71
 
< 0.1%
Other values (545) 2361
 
0.4%
(Missing) 437162
80.0%

BHF_velo
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct159
Distinct (%)0.1%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
108703 
Zürich HB
 
79
Bern
 
73
Basel SBB
 
65
Luzern
 
54
Other values (154)
 
507

Unique

Unique86 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108703
 
19.9%
Zürich HB 79
 
< 0.1%
Bern 73
 
< 0.1%
Basel SBB 65
 
< 0.1%
Luzern 54
 
< 0.1%
Genève 30
 
< 0.1%
Aarau 23
 
< 0.1%
Lausanne 23
 
< 0.1%
Winterthur 22
 
< 0.1%
Thun 18
 
< 0.1%
Other values (149) 391
 
0.1%
(Missing) 437162
80.0%

BHF_velo_non
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
quoted
61436 
-77
44193 
not quoted
 
3850

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 61436
 
11.2%
-77 44193
 
8.1%
not quoted 3850
 
0.7%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:44.171420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_velo_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
54931 
not quoted
51716 
quoted
 
2832

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownot quoted
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 54931
 
10.0%
not quoted 51716
 
9.5%
quoted 2832
 
0.5%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:44.284330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_velo_ziel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
66557 
not quoted
41799 
quoted
 
1123

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 66557
 
12.2%
not quoted 41799
 
7.6%
quoted 1123
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:44.457304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

BHF_wc
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct69
Distinct (%)0.1%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
109338 
Zürich HB
 
14
Luzern
 
9
Olten
 
7
Basel SBB
 
7
Other values (64)
 
106

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109338
 
20.0%
Zürich HB 14
 
< 0.1%
Luzern 9
 
< 0.1%
Olten 7
 
< 0.1%
Basel SBB 7
 
< 0.1%
Genève 6
 
< 0.1%
St. Gallen 5
 
< 0.1%
Chur 5
 
< 0.1%
Bern 5
 
< 0.1%
Visp 4
 
< 0.1%
Other values (59) 81
 
< 0.1%
(Missing) 437162
80.0%

BHF_wegweisung
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct441
Distinct (%)0.4%
Missing437162
Missing (%)80.0%
Memory size4.2 MiB
-66
106838 
Zürich HB
 
323
Bern
 
194
Lausanne
 
160
Olten
 
96
Other values (436)
 
1870

Unique

Unique244 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 106838
 
19.5%
Zürich HB 323
 
0.1%
Bern 194
 
< 0.1%
Lausanne 160
 
< 0.1%
Olten 96
 
< 0.1%
Genève 86
 
< 0.1%
Liestal 74
 
< 0.1%
Basel SBB 71
 
< 0.1%
Genève-Aéroport 50
 
< 0.1%
Luzern 49
 
< 0.1%
Other values (431) 1540
 
0.3%
(Missing) 437162
80.0%

createdAt
Categorical

Distinct461329
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2019-10-09 16:54:48.930
 
4
2019-10-09 16:32:37.180
 
4
2019-10-09 16:28:13.393
 
4
2019-10-09 16:53:07.930
 
4
2019-10-09 15:45:03.460
 
4
Other values (461324)
546623 

Unique

Unique395320 ?
Unique (%)72.3%

Sample

1st row2019-07-19 22:15:07.430
2nd row2019-07-19 22:15:07.803
3rd row2019-07-19 22:15:07.920
4th row2019-07-19 22:15:08.033
5th row2019-07-19 22:15:08.143

Common Values

ValueCountFrequency (%)
2019-10-09 16:54:48.930 4
 
< 0.1%
2019-10-09 16:32:37.180 4
 
< 0.1%
2019-10-09 16:28:13.393 4
 
< 0.1%
2019-10-09 16:53:07.930 4
 
< 0.1%
2019-10-09 15:45:03.460 4
 
< 0.1%
2019-10-09 16:29:50.017 4
 
< 0.1%
2019-10-09 16:12:06.233 4
 
< 0.1%
2019-10-09 15:27:46.783 4
 
< 0.1%
2019-10-09 16:42:46.187 4
 
< 0.1%
2019-10-09 15:36:49.860 4
 
< 0.1%
Other values (461319) 546603
> 99.9%

date_of_first_mail
Categorical

HIGH CARDINALITY  MISSING 

Distinct47624
Distinct (%)26.6%
Missing367358
Missing (%)67.2%
Memory size4.2 MiB
2021-06-28 18:04:16
 
23
2022-07-15 14:40:36
 
20
2022-06-22 17:20:09
 
20
2022-09-28 19:01:44
 
19
2022-11-02 19:30:22
 
19
Other values (47619)
179184 

Unique

Unique12190 ?
Unique (%)6.8%

Sample

1st row2020-11-11 16:30:22
2nd row2020-11-11 16:30:23
3rd row2020-11-11 16:30:23
4th row2020-11-11 16:30:24
5th row2020-11-11 16:30:24

Common Values

ValueCountFrequency (%)
2021-06-28 18:04:16 23
 
< 0.1%
2022-07-15 14:40:36 20
 
< 0.1%
2022-06-22 17:20:09 20
 
< 0.1%
2022-09-28 19:01:44 19
 
< 0.1%
2022-11-02 19:30:22 19
 
< 0.1%
2021-09-29 17:40:57 18
 
< 0.1%
2022-08-29 13:11:23 18
 
< 0.1%
2022-08-17 14:50:15 18
 
< 0.1%
2022-08-15 09:31:01 18
 
< 0.1%
2022-08-17 15:00:18 18
 
< 0.1%
Other values (47614) 179094
32.8%
(Missing) 367358
67.2%

date_of_last_access
Categorical

HIGH CARDINALITY  MISSING 

Distinct378420
Distinct (%)99.3%
Missing165697
Missing (%)30.3%
Memory size4.2 MiB
2021-09-21 08:44:30
 
3
2020-11-02 18:35:27
 
3
2019-04-15 17:39:57
 
3
2019-03-25 19:34:43
 
3
2021-09-15 19:02:21
 
3
Other values (378415)
380931 

Unique

Unique375915 ?
Unique (%)98.7%

Sample

1st row2018-07-04 19:10:49
2nd row2018-07-04 07:31:05
3rd row2018-07-04 14:38:25
4th row2018-07-04 08:05:31
5th row2018-07-04 08:41:28

Common Values

ValueCountFrequency (%)
2021-09-21 08:44:30 3
 
< 0.1%
2020-11-02 18:35:27 3
 
< 0.1%
2019-04-15 17:39:57 3
 
< 0.1%
2019-03-25 19:34:43 3
 
< 0.1%
2021-09-15 19:02:21 3
 
< 0.1%
2022-10-31 14:54:20 3
 
< 0.1%
2020-06-09 15:42:29 3
 
< 0.1%
2022-07-19 12:42:14 3
 
< 0.1%
2019-11-13 17:37:08 3
 
< 0.1%
2022-08-08 12:08:13 3
 
< 0.1%
Other values (378410) 380916
69.7%
(Missing) 165697
30.3%

device_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing328380
Missing (%)60.1%
Memory size4.2 MiB
Desktop
151030 
Smartphone
67233 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowDesktop

Common Values

ValueCountFrequency (%)
Desktop 151030
27.6%
Smartphone 67233
 
12.3%
(Missing) 328380
60.1%

Common Values (Plot)

2023-01-04T17:47:44.582905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

dispcode
Categorical

Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)60.1%
Memory size4.2 MiB
Beendet
166452 
Ausgescreent
47237 
Beendet nach Unterbrechung
 
4574

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBeendet
2nd rowBeendet
3rd rowBeendet
4th rowBeendet
5th rowBeendet

Common Values

ValueCountFrequency (%)
Beendet 166452
30.4%
Ausgescreent 47237
 
8.6%
Beendet nach Unterbrechung 4574
 
0.8%
(Missing) 328380
60.1%

Common Values (Plot)

2023-01-04T17:47:44.698176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EKL_fehlende_infos_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct693
Distinct (%)0.2%
Missing219109
Missing (%)40.1%
Memory size4.2 MiB
-66
326711 
-99
 
114
App
 
11
SBB App
 
4
Smartphone
 
3
Other values (688)
 
691

Unique

Unique685 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 326711
59.8%
-99 114
 
< 0.1%
App 11
 
< 0.1%
SBB App 4
 
< 0.1%
Smartphone 3
 
< 0.1%
SMS 2
 
< 0.1%
mobile 2
 
< 0.1%
Durchsage im Zug 2
 
< 0.1%
konkret, wie ich alternativ weiterreisen kann, auf meinem Smartphone 1
 
< 0.1%
schnellster Weg nach Thun ab Wankdorf (zurück nach Bern?, warten?) 1
 
< 0.1%
Other values (683) 683
 
0.1%
(Missing) 219109
40.1%

EKL_info_nutzung
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
324728 
ja, ich habe den vorgeschlagenen Reiseweg gewählt
 
2338
ja, ich bin später gereist
 
784
ja, ich habe einen anderen als den vorgeschlagenen Reiseweg gewählt
 
300
nein
 
159
Other values (2)
 
71

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 324728
59.4%
ja, ich habe den vorgeschlagenen Reiseweg gewählt 2338
 
0.4%
ja, ich bin später gereist 784
 
0.1%
ja, ich habe einen anderen als den vorgeschlagenen Reiseweg gewählt 300
 
0.1%
nein 159
 
< 0.1%
ja, ich habe meine Reise abgebrochen / nicht angetreten 40
 
< 0.1%
weiss nicht 31
 
< 0.1%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:44.827236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EKL_info_vermisst
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
324257 
Ja
 
2132
Nein
 
1733
Weiss nicht
 
258

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 324257
59.3%
Ja 2132
 
0.4%
Nein 1733
 
0.3%
Weiss nicht 258
 
< 0.1%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:44.968357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EKL_info_zielerreichung
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
320123 
Nein
 
4150
Ja
 
3723
Weiss nicht
 
384

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 320123
58.6%
Nein 4150
 
0.8%
Ja 3723
 
0.7%
Weiss nicht 384
 
0.1%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:45.084897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EKL_reiseaenderung
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
302975 
Nein
 
16899
Ja
 
8248
Weiss nicht
 
258

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 302975
55.4%
Nein 16899
 
3.1%
Ja 8248
 
1.5%
Weiss nicht 258
 
< 0.1%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:45.200135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_allgemein
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
10
88998 
8
87621 
-77
79373 
9
77460 
5
70235 
Other values (7)
142956 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row9
3rd row10
4th row8
5th row10

Common Values

ValueCountFrequency (%)
10 88998
16.3%
8 87621
16.0%
-77 79373
14.5%
9 77460
14.2%
5 70235
12.8%
4 65293
11.9%
7 37476
6.9%
3 16630
 
3.0%
6 13062
 
2.4%
weiss nicht 5388
 
1.0%
Other values (2) 5107
 
0.9%

OES_zuf_personal_angemessen
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing109481
Missing (%)20.0%
Memory size4.2 MiB
-77
359850 
10
 
23266
99
 
19703
9
 
9974
8
 
8758
Other values (8)
 
15611

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 359850
65.8%
10 23266
 
4.3%
99 19703
 
3.6%
9 9974
 
1.8%
8 8758
 
1.6%
5 7489
 
1.4%
4 3001
 
0.5%
7 2880
 
0.5%
6 1167
 
0.2%
3 651
 
0.1%
Other values (3) 423
 
0.1%
(Missing) 109481
 
20.0%

OES_zuf_personal_aufmerksam
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing109481
Missing (%)20.0%
Memory size4.2 MiB
-77
359850 
weiss nicht
 
23792
10
 
12255
8
 
12037
9
 
8585
Other values (8)
 
20643

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 359850
65.8%
weiss nicht 23792
 
4.4%
10 12255
 
2.2%
8 12037
 
2.2%
9 8585
 
1.6%
5 5874
 
1.1%
7 5651
 
1.0%
4 4488
 
0.8%
6 2482
 
0.5%
3 1448
 
0.3%
Other values (3) 700
 
0.1%
(Missing) 109481
 
20.0%

OES_zuf_personal_effekt
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing109481
Missing (%)20.0%
Memory size4.2 MiB
-77
359850 
10
 
17564
8
 
11551
weiss nicht
 
10658
5
 
10620
Other values (8)
 
26919

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 359850
65.8%
10 17564
 
3.2%
8 11551
 
2.1%
weiss nicht 10658
 
1.9%
5 10620
 
1.9%
9 8510
 
1.6%
7 5997
 
1.1%
4 4652
 
0.9%
6 4318
 
0.8%
3 2652
 
0.5%
Other values (3) 790
 
0.1%
(Missing) 109481
 
20.0%

wime_gesamtzuf
Categorical

Distinct13
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
10
109049 
5
99882 
9
83651 
8
68393 
4
61124 
Other values (8)
124543 

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row8
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 109049
19.9%
5 99882
18.3%
9 83651
15.3%
8 68393
12.5%
4 61124
11.2%
-77 56613
10.4%
7 28971
 
5.3%
3 14849
 
2.7%
6 11899
 
2.2%
1 5483
 
1.0%
Other values (3) 6728
 
1.2%
Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
5
34642 
-77
31181 
4
29687 
3
9206 
2
 
2130
Other values (2)
 
2633

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 34642
 
6.3%
-77 31181
 
5.7%
4 29687
 
5.4%
3 9206
 
1.7%
2 2130
 
0.4%
weiss nicht 2066
 
0.4%
1 567
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:45.332111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
5
31947 
-77
31181 
4
27248 
weiss nicht
8251 
3
8062 
Other values (2)
 
2790

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 31947
 
5.8%
-77 31181
 
5.7%
4 27248
 
5.0%
weiss nicht 8251
 
1.5%
3 8062
 
1.5%
2 1987
 
0.4%
1 803
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:45.477871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Sicherheit_BhfStart
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
102796 
5
 
2469
4
 
2128
weiss nicht
 
1513
3
 
474
Other values (2)
 
99

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 102796
 
18.8%
5 2469
 
0.5%
4 2128
 
0.4%
weiss nicht 1513
 
0.3%
3 474
 
0.1%
2 66
 
< 0.1%
1 33
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:45.621518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_WC_BhfStart
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108801 
4
 
239
5
 
200
3
 
112
2
 
53
Other values (2)
 
74

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108801
 
19.9%
4 239
 
< 0.1%
5 200
 
< 0.1%
3 112
 
< 0.1%
2 53
 
< 0.1%
1 39
 
< 0.1%
weiss nicht 35
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:45.760674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Velo_BhfStart
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106647 
4
 
748
5
 
682
3
 
629
2
 
395
Other values (2)
 
378

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106647
 
19.5%
4 748
 
0.1%
5 682
 
0.1%
3 629
 
0.1%
2 395
 
0.1%
1 222
 
< 0.1%
weiss nicht 156
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:45.894989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Park_BhfStart
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106343 
5
 
1418
4
 
922
3
 
358
2
 
178
Other values (2)
 
260

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106343
 
19.5%
5 1418
 
0.3%
4 922
 
0.2%
3 358
 
0.1%
2 178
 
< 0.1%
weiss nicht 133
 
< 0.1%
1 127
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.033867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Share_BhfStart
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
109233 
weiss nicht
 
85
5
 
71
4
 
52
3
 
22
Other values (2)
 
16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 109233
 
20.0%
weiss nicht 85
 
< 0.1%
5 71
 
< 0.1%
4 52
 
< 0.1%
3 22
 
< 0.1%
2 11
 
< 0.1%
1 5
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.173495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
5
32815 
-77
31181 
4
29984 
3
10087 
weiss nicht
 
2534
Other values (2)
 
2878

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 32815
 
6.0%
-77 31181
 
5.7%
4 29984
 
5.5%
3 10087
 
1.8%
weiss nicht 2534
 
0.5%
2 2302
 
0.4%
1 576
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.317549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
5
31610 
-77
31181 
4
26693 
3
8614 
weiss nicht
8028 
Other values (2)
3353 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row-77
4th row4
5th row3

Common Values

ValueCountFrequency (%)
5 31610
 
5.8%
-77 31181
 
5.7%
4 26693
 
4.9%
3 8614
 
1.6%
weiss nicht 8028
 
1.5%
2 2398
 
0.4%
1 955
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.460945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Sicherheit_BhfZiel
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
103955 
5
 
2140
4
 
1815
weiss nicht
 
1056
3
 
414
Other values (2)
 
99

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 103955
 
19.0%
5 2140
 
0.4%
4 1815
 
0.3%
weiss nicht 1056
 
0.2%
3 414
 
0.1%
2 67
 
< 0.1%
1 32
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.601274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_WC_BhfZiel
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108808 
4
 
231
5
 
222
3
 
112
2
 
45
Other values (2)
 
61

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108808
 
19.9%
4 231
 
< 0.1%
5 222
 
< 0.1%
3 112
 
< 0.1%
2 45
 
< 0.1%
1 31
 
< 0.1%
weiss nicht 30
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.736792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Velo_BhfZiel
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108356 
5
 
292
4
 
281
3
 
217
2
 
158
Other values (2)
 
175

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108356
 
19.8%
5 292
 
0.1%
4 281
 
0.1%
3 217
 
< 0.1%
2 158
 
< 0.1%
weiss nicht 89
 
< 0.1%
1 86
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.866580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Park_BhfZiel
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108272 
5
 
561
4
 
342
3
 
136
weiss nicht
 
82
Other values (2)
 
86

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108272
 
19.8%
5 561
 
0.1%
4 342
 
0.1%
3 136
 
< 0.1%
weiss nicht 82
 
< 0.1%
2 48
 
< 0.1%
1 38
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:46.993629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Wime_Share_BhfZiel
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
109273 
5
 
74
4
 
60
weiss nicht
 
40
3
 
23
Other values (2)
 
9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 109273
 
20.0%
5 74
 
< 0.1%
4 60
 
< 0.1%
weiss nicht 40
 
< 0.1%
3 23
 
< 0.1%
1 7
 
< 0.1%
2 2
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:47.131237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

wime_sicherheitskraft
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
107257 
5
 
1025
weiss nicht
 
563
4
 
421
3
 
154
Other values (2)
 
59

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 107257
 
19.6%
5 1025
 
0.2%
weiss nicht 563
 
0.1%
4 421
 
0.1%
3 154
 
< 0.1%
1 36
 
< 0.1%
2 23
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:47.325107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

fg_abfahrt
Categorical

HIGH CARDINALITY  MISSING 

Distinct1400
Distinct (%)0.3%
Missing41207
Missing (%)7.5%
Memory size4.2 MiB
17:02:00
 
1664
17:04:00
 
1581
17:00:00
 
1478
16:02:00
 
1471
07:32:00
 
1470
Other values (1395)
497772 

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row09:52:00
2nd row17:04:00
3rd row16:42:00
4th row13:06:00
5th row17:43:00

Common Values

ValueCountFrequency (%)
17:02:00 1664
 
0.3%
17:04:00 1581
 
0.3%
17:00:00 1478
 
0.3%
16:02:00 1471
 
0.3%
07:32:00 1470
 
0.3%
17:34:00 1405
 
0.3%
17:32:00 1383
 
0.3%
16:32:00 1376
 
0.3%
07:34:00 1325
 
0.2%
16:34:00 1286
 
0.2%
Other values (1390) 490997
89.8%
(Missing) 41207
 
7.5%

fg_ankunft
Categorical

HIGH CARDINALITY  MISSING 

Distinct1416
Distinct (%)0.3%
Missing41209
Missing (%)7.5%
Memory size4.2 MiB
17:56:00
 
1570
18:28:00
 
1479
18:00:00
 
1331
17:58:00
 
1318
17:28:00
 
1303
Other values (1411)
498433 

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st row10:39:00
2nd row17:50:00
3rd row18:45:00
4th row13:57:00
5th row17:55:00

Common Values

ValueCountFrequency (%)
17:56:00 1570
 
0.3%
18:28:00 1479
 
0.3%
18:00:00 1331
 
0.2%
17:58:00 1318
 
0.2%
17:28:00 1303
 
0.2%
18:56:00 1281
 
0.2%
08:56:00 1248
 
0.2%
18:25:00 1227
 
0.2%
16:56:00 1203
 
0.2%
07:56:00 1200
 
0.2%
Other values (1406) 492274
90.1%
(Missing) 41209
 
7.5%

fg_startort
Categorical

Distinct16108
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Zürich HB
 
35050
Bern
 
23319
Basel SBB
 
19036
Zürich Flughafen
 
17180
Luzern
 
14214
Other values (16103)
437844 

Unique

Unique4411 ?
Unique (%)0.8%

Sample

1st rowUrdorf, Neumatt
2nd rowRigi Kulm
3rd rowPorrentruy
4th rowBern
5th rowDiessenhofen

Common Values

ValueCountFrequency (%)
Zürich HB 35050
 
6.4%
Bern 23319
 
4.3%
Basel SBB 19036
 
3.5%
Zürich Flughafen 17180
 
3.1%
Luzern 14214
 
2.6%
Genève 13350
 
2.4%
Lausanne 12260
 
2.2%
-66 9996
 
1.8%
Genève-Aéroport 7111
 
1.3%
Olten 6083
 
1.1%
Other values (16098) 389044
71.2%

fg_startort_uic
Categorical

Distinct14385
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
 
41182
8503000
 
33716
8507000
 
22527
8500010
 
18224
8503016
 
16635
Other values (14380)
414359 

Unique

Unique3763 ?
Unique (%)0.7%

Sample

1st row8590841
2nd row8505069
3rd row8500126
4th row8507000
5th row8503428

Common Values

ValueCountFrequency (%)
-66 41182
 
7.5%
8503000 33716
 
6.2%
8507000 22527
 
4.1%
8500010 18224
 
3.3%
8503016 16635
 
3.0%
8505000 13671
 
2.5%
8501008 12627
 
2.3%
8501120 11727
 
2.1%
8501026 6872
 
1.3%
8500218 5824
 
1.1%
Other values (14375) 363638
66.5%

fg_teilstr
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
1
212431 
2
119558 
3
107398 
-66
41182 
5
28477 
Other values (10)
37597 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 212431
38.9%
2 119558
21.9%
3 107398
19.6%
-66 41182
 
7.5%
5 28477
 
5.2%
4 24373
 
4.5%
7 7458
 
1.4%
6 2972
 
0.5%
9 1823
 
0.3%
8 443
 
0.1%
Other values (5) 528
 
0.1%

fg_via
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct50375
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
190786 
-99
62833 
<div>Zürich HB</div>
 
15487
Zürich HB
 
10078
<div>Bern</div>
 
6921
Other values (50370)
260538 

Unique

Unique33499 ?
Unique (%)6.1%

Sample

1st row<div>Schlieren, Bahnhof</div>
2nd row-66
3rd row<div>Biel/Bienne</div>
4th row<div>Spiez</div>
5th row-66

Common Values

ValueCountFrequency (%)
-66 190786
34.9%
-99 62833
 
11.5%
<div>Zürich HB</div> 15487
 
2.8%
Zürich HB 10078
 
1.8%
<div>Bern</div> 6921
 
1.3%
<div>Olten</div> 5475
 
1.0%
Bern 4499
 
0.8%
Olten 3731
 
0.7%
<div>Luzern</div> 3332
 
0.6%
<div>Lausanne</div> 3019
 
0.6%
Other values (50365) 240482
44.0%

fg_vm
Categorical

Distinct159831
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
undefined
194598 
-66
41182 
IR 90
 
2030
IC 5
 
1935
IC 1
 
1715
Other values (159826)
305183 

Unique

Unique132020 ?
Unique (%)24.2%

Sample

1st rowundefined
2nd rowundefined
3rd rowundefined
4th rowundefined
5th rowundefined

Common Values

ValueCountFrequency (%)
undefined 194598
35.6%
-66 41182
 
7.5%
IR 90 2030
 
0.4%
IC 5 1935
 
0.4%
IC 1 1715
 
0.3%
IR 15 1166
 
0.2%
IC 8 1147
 
0.2%
S 5 998
 
0.2%
S 1 967
 
0.2%
S 3 862
 
0.2%
Other values (159821) 300043
54.9%

fg_zielort
Categorical

Distinct15909
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Zürich HB
 
42108
Bern
 
26655
Basel SBB
 
18244
Luzern
 
16059
Zürich Flughafen
 
14668
Other values (15904)
428909 

Unique

Unique4651 ?
Unique (%)0.9%

Sample

1st rowWinterthur
2nd rowArth-Goldau RB
3rd rowLausanne
4th rowFaulensee, Dorf
5th rowStein am Rhein

Common Values

ValueCountFrequency (%)
Zürich HB 42108
 
7.7%
Bern 26655
 
4.9%
Basel SBB 18244
 
3.3%
Luzern 16059
 
2.9%
Zürich Flughafen 14668
 
2.7%
Lausanne 13880
 
2.5%
Genève 12867
 
2.4%
-66 9994
 
1.8%
Winterthur 7177
 
1.3%
St. Gallen 7149
 
1.3%
Other values (15899) 377842
69.1%

fg_zielort_uic
Categorical

Distinct14161
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
41182 
8503000
 
40379
8507000
 
25749
8500010
 
17376
8505000
 
15422
Other values (14156)
406535 

Unique

Unique3966 ?
Unique (%)0.7%

Sample

1st row8506000
2nd row8505063
3rd row8501120
4th row8588606
5th row8506139

Common Values

ValueCountFrequency (%)
-66 41182
 
7.5%
8503000 40379
 
7.4%
8507000 25749
 
4.7%
8500010 17376
 
3.2%
8505000 15422
 
2.8%
8503016 14249
 
2.6%
8501120 13241
 
2.4%
8501008 12163
 
2.2%
8506302 6925
 
1.3%
8506000 6829
 
1.2%
Other values (14151) 353128
64.6%

ft_abfahrt
Categorical

HIGH CARDINALITY  MISSING 

Distinct1394
Distinct (%)0.3%
Missing55368
Missing (%)10.1%
Memory size4.2 MiB
17:02:00
 
1963
07:32:00
 
1792
17:04:00
 
1771
17:34:00
 
1725
16:02:00
 
1683
Other values (1389)
482341 

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st row10:04:00
2nd row17:04:00
3rd row17:45:00
4th row13:06:00
5th row17:43:00

Common Values

ValueCountFrequency (%)
17:02:00 1963
 
0.4%
07:32:00 1792
 
0.3%
17:04:00 1771
 
0.3%
17:34:00 1725
 
0.3%
16:02:00 1683
 
0.3%
17:32:00 1651
 
0.3%
17:00:00 1632
 
0.3%
07:34:00 1624
 
0.3%
16:32:00 1612
 
0.3%
16:34:00 1597
 
0.3%
Other values (1384) 474225
86.8%
(Missing) 55368
 
10.1%

ft_ankunft
Categorical

HIGH CARDINALITY  MISSING 

Distinct1407
Distinct (%)0.3%
Missing55368
Missing (%)10.1%
Memory size4.2 MiB
18:28:00
 
1971
17:56:00
 
1958
07:56:00
 
1839
17:28:00
 
1831
16:56:00
 
1700
Other values (1402)
481976 

Unique

Unique46 ?
Unique (%)< 0.1%

Sample

1st row10:39:00
2nd row17:50:00
3rd row18:45:00
4th row13:34:00
5th row17:55:00

Common Values

ValueCountFrequency (%)
18:28:00 1971
 
0.4%
17:56:00 1958
 
0.4%
07:56:00 1839
 
0.3%
17:28:00 1831
 
0.3%
16:56:00 1700
 
0.3%
08:56:00 1674
 
0.3%
08:28:00 1661
 
0.3%
17:58:00 1648
 
0.3%
18:00:00 1640
 
0.3%
08:24:00 1599
 
0.3%
Other values (1397) 473754
86.7%
(Missing) 55368
 
10.1%

ft_haltestellen
Categorical

HIGH CARDINALITY  MISSING 

Distinct25283
Distinct (%)6.6%
Missing165697
Missing (%)30.3%
Memory size4.2 MiB
-66
55343 
Bern - Zürich HB
 
4018
Zürich HB - Bern
 
4016
Basel SBB - Zürich HB
 
3198
Zürich Flughafen - Zürich HB
 
2472
Other values (25278)
311899 

Unique

Unique9843 ?
Unique (%)2.6%

Sample

1st rowSchlieren - Zürich Altstetten - Zürich Hardbrücke - Zürich HB - Zürich Stadelhofen - Stettbach - Winterthur
2nd rowRigi Kulm - Rigi Staffel - Rigi Wölfertschen-First - Rigi Klösterli - Fruttli - Kräbel - Goldau A4 - Arth-Goldau RB
3rd rowBiel/Bienne - Neuchâtel - Yverdon-les-Bains - Lausanne
4th rowBern - Thun - Spiez
5th rowDiessenhofen - Schlattingen - Etzwilen - Stein am Rhein

Common Values

ValueCountFrequency (%)
-66 55343
 
10.1%
Bern - Zürich HB 4018
 
0.7%
Zürich HB - Bern 4016
 
0.7%
Basel SBB - Zürich HB 3198
 
0.6%
Zürich Flughafen - Zürich HB 2472
 
0.5%
Zürich HB - Basel SBB 2369
 
0.4%
Chur - Landquart - Sargans - Zürich HB 1881
 
0.3%
Landquart - Sargans - Zürich HB 1796
 
0.3%
Zürich HB - Sargans - Landquart - Chur 1575
 
0.3%
Olten - Bern 1426
 
0.3%
Other values (25273) 302852
55.4%
(Missing) 165697
30.3%

ft_startort
Categorical

Distinct7705
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Zürich HB
50787 
Bern
 
30721
-66
 
24225
Basel SBB
 
21068
Zürich Flughafen
 
17921
Other values (7700)
401921 

Unique

Unique3376 ?
Unique (%)0.6%

Sample

1st rowSchlieren, Bahnhof
2nd rowRigi Kulm
3rd rowBiel/Bienne
4th rowBern
5th rowDiessenhofen

Common Values

ValueCountFrequency (%)
Zürich HB 50787
 
9.3%
Bern 30721
 
5.6%
-66 24225
 
4.4%
Basel SBB 21068
 
3.9%
Zürich Flughafen 17921
 
3.3%
Luzern 16870
 
3.1%
Lausanne 15326
 
2.8%
Genève 13835
 
2.5%
Olten 12240
 
2.2%
Winterthur 8995
 
1.6%
Other values (7695) 334655
61.2%

ft_startort_uic
Categorical

Distinct2843
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
55343 
8503000
49463 
8507000
 
29934
8500010
 
20261
8503016
 
17377
Other values (2838)
374265 

Unique

Unique337 ?
Unique (%)0.1%

Sample

1st row8590786
2nd row8505069
3rd row8504300
4th row8507000
5th row8503428

Common Values

ValueCountFrequency (%)
-66 55343
 
10.1%
8503000 49463
 
9.0%
8507000 29934
 
5.5%
8500010 20261
 
3.7%
8503016 17377
 
3.2%
8505000 16330
 
3.0%
8501120 14796
 
2.7%
8501008 13115
 
2.4%
8500218 11984
 
2.2%
8506000 8668
 
1.6%
Other values (2833) 309372
56.6%

ft_tu
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct60
Distinct (%)< 0.1%
Missing165697
Missing (%)30.3%
Memory size4.2 MiB
SBB
265327 
-66
55343 
BLS
 
14382
SOB
 
9384
THU
 
8552
Other values (55)
27958 

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowSBB
2nd rowRB
3rd rowSBB
4th rowSBB
5th rowTHU

Common Values

ValueCountFrequency (%)
SBB 265327
48.5%
-66 55343
 
10.1%
BLS 14382
 
2.6%
SOB 9384
 
1.7%
THU 8552
 
1.6%
RhB 6021
 
1.1%
ZB 4492
 
0.8%
MGB 2519
 
0.5%
RA 2306
 
0.4%
TPF 1485
 
0.3%
Other values (50) 11135
 
2.0%
(Missing) 165697
30.3%

ft_uic_haltestellen
Categorical

HIGH CARDINALITY  MISSING 

Distinct33431
Distinct (%)8.8%
Missing165697
Missing (%)30.3%
Memory size4.2 MiB
-66
55343 
8507000,8503000
 
2186
8503000,8507000
 
2099
8503000, 8507000
 
1917
8507000, 8503000
 
1832
Other values (33426)
317569 

Unique

Unique13801 ?
Unique (%)3.6%

Sample

1st row8503509,8503001,8503020,8503000,8503003,8503147,8506000
2nd row8505069,8505068,8505067,8505066,8505065,8505064,8505062,8505063
3rd row8504300,8504221,8504200,8501120
4th row8507000,8507100,8507483
5th row8503428,8503429,8506025,8506139

Common Values

ValueCountFrequency (%)
-66 55343
 
10.1%
8507000,8503000 2186
 
0.4%
8503000,8507000 2099
 
0.4%
8503000, 8507000 1917
 
0.4%
8507000, 8503000 1832
 
0.3%
8503016,8503000 1784
 
0.3%
8500010,8503000 1699
 
0.3%
8500010, 8503000 1499
 
0.3%
8503000,8500010 1289
 
0.2%
8503000, 8500010 1080
 
0.2%
Other values (33421) 310218
56.7%
(Missing) 165697
30.3%

ft_vm
Categorical

Distinct31234
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
55343 
IC 5
 
3706
IC 1
 
2279
IC 8
 
2212
IR 90
 
2035
Other values (31229)
481068 

Unique

Unique9446 ?
Unique (%)1.7%

Sample

1st rowS 12
2nd rowR 170
3rd rowIC 5
4th rowIC 8
5th rowS 8

Common Values

ValueCountFrequency (%)
-66 55343
 
10.1%
IC 5 3706
 
0.7%
IC 1 2279
 
0.4%
IC 8 2212
 
0.4%
IR 90 2035
 
0.4%
IR 70 1678
 
0.3%
IC 3 1329
 
0.2%
IR 15 1232
 
0.2%
IC 61 1225
 
0.2%
IR 36 958
 
0.2%
Other values (31224) 474646
86.8%

ft_vm_code
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
3
317979 
1
139117 
-66
55343 
2
34204 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 317979
58.2%
1 139117
25.4%
-66 55343
 
10.1%
2 34204
 
6.3%

Common Values (Plot)

2023-01-04T17:47:47.480994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

ft_vm_kurz
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
IC
144094 
IR
139405 
S
117677 
-66
55343 
RE
34201 
Other values (10)
55923 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowR
3rd rowIC
4th rowIC
5th rowS

Common Values

ValueCountFrequency (%)
IC 144094
26.4%
IR 139405
25.5%
S 117677
21.5%
-66 55343
 
10.1%
RE 34201
 
6.3%
R 21131
 
3.9%
EC 14126
 
2.6%
ICN 12605
 
2.3%
ICE 6247
 
1.1%
TGV 1403
 
0.3%
Other values (5) 411
 
0.1%

ft_zielort
Categorical

Distinct6649
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Zürich HB
82500 
Bern
42997 
-66
 
24223
Luzern
 
22364
Lausanne
 
21643
Other values (6644)
352916 

Unique

Unique3266 ?
Unique (%)0.6%

Sample

1st rowWinterthur
2nd rowArth-Goldau RB
3rd rowLausanne
4th rowSpiez
5th rowStein am Rhein

Common Values

ValueCountFrequency (%)
Zürich HB 82500
 
15.1%
Bern 42997
 
7.9%
-66 24223
 
4.4%
Luzern 22364
 
4.1%
Lausanne 21643
 
4.0%
Olten 19836
 
3.6%
Basel SBB 18265
 
3.3%
Genève 12394
 
2.3%
Zürich Flughafen 11726
 
2.1%
Winterthur 10469
 
1.9%
Other values (6639) 280226
51.3%

ft_zielort_uic
Categorical

Distinct1836
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
8503000
80787 
-66
55343 
8507000
42100 
8505000
 
21731
8501120
 
21005
Other values (1831)
325677 

Unique

Unique185 ?
Unique (%)< 0.1%

Sample

1st row8506000
2nd row8505063
3rd row8501120
4th row8507483
5th row8506139

Common Values

ValueCountFrequency (%)
8503000 80787
 
14.8%
-66 55343
 
10.1%
8507000 42100
 
7.7%
8505000 21731
 
4.0%
8501120 21005
 
3.8%
8500218 19593
 
3.6%
8500010 17399
 
3.2%
8501008 11695
 
2.1%
8503016 11306
 
2.1%
8506000 10122
 
1.9%
Other values (1826) 255562
46.8%

ft_zug_nr
Categorical

Distinct20127
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
55343 
5
 
4550
1
 
3065
8
 
2857
3
 
2461
Other values (20122)
478367 

Unique

Unique4657 ?
Unique (%)0.9%

Sample

1st row12
2nd row170
3rd row5
4th row8
5th row8

Common Values

ValueCountFrequency (%)
55343
 
10.1%
5 4550
 
0.8%
1 3065
 
0.6%
8 2857
 
0.5%
3 2461
 
0.5%
90 2048
 
0.4%
70 1951
 
0.4%
15 1799
 
0.3%
61 1250
 
0.2%
2 1249
 
0.2%
Other values (20117) 470070
86.0%

Kommentar
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct146839
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
286013 
-99
109790 
Nein
 
227
-
 
199
 
187
Other values (146834)
150227 

Unique

Unique145886 ?
Unique (%)26.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 286013
52.3%
-99 109790
 
20.1%
Nein 227
 
< 0.1%
- 199
 
< 0.1%
187
 
< 0.1%
nein 105
 
< 0.1%
Keine 102
 
< 0.1%
. 81
 
< 0.1%
Non 79
 
< 0.1%
No 71
 
< 0.1%
Other values (146829) 149789
27.4%

OES_beeintraechtigung
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
Nein
307229 
Ja
 
11820
Weiss nicht
 
5324
-77
 
4007

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein 307229
56.2%
Ja 11820
 
2.2%
Weiss nicht 5324
 
1.0%
-77 4007
 
0.7%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:47.616461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
529096 
not quoted
 
10206
quoted
 
7341

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 529096
96.8%
not quoted 10206
 
1.9%
quoted 7341
 
1.3%

Common Values (Plot)

2023-01-04T17:47:47.733668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_2
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
317022 
not quoted
 
10027
quoted
 
1331

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 317022
58.0%
not quoted 10027
 
1.8%
quoted 1331
 
0.2%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:47.846945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
529096 
not quoted
 
13337
quoted
 
4210

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 529096
96.8%
not quoted 13337
 
2.4%
quoted 4210
 
0.8%

Common Values (Plot)

2023-01-04T17:47:47.952340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
529096 
not quoted
 
12281
quoted
 
5266

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 529096
96.8%
not quoted 12281
 
2.2%
quoted 5266
 
1.0%

Common Values (Plot)

2023-01-04T17:47:48.053404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_5
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
212073 
not quouted
 
4892
quoted
 
1297

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 212073
38.8%
not quouted 4892
 
0.9%
quoted 1297
 
0.2%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:48.165641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_6
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
212073 
not quouted
 
5717
quoted
 
472

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 212073
38.8%
not quouted 5717
 
1.0%
quoted 472
 
0.1%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:48.279445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_7
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108949 
not quoted
 
518
quoted
 
12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108949
 
19.9%
not quoted 518
 
0.1%
quoted 12
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:48.386583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_7_txt
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109473 
Boh
 
1
Je pense qu'on est réfugiés en Suisse, on ne peut pas nous exprimer et les sécurité ne seront pas encore discuter avec nous...
 
1
Al equipaggio comunque chi e sul bordo del binario… Gente non informata
 
1
yv<df
 
1
Other values (2)
 
2

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109473
 
20.0%
Boh 1
 
< 0.1%
Je pense qu'on est réfugiés en Suisse, on ne peut pas nous exprimer et les sécurité ne seront pas encore discuter avec nous... 1
 
< 0.1%
Al equipaggio comunque chi e sul bordo del binario… Gente non informata 1
 
< 0.1%
yv<df 1
 
< 0.1%
Siehe Kommentar bezüglich Fantrennung 1
 
< 0.1%
Il cane antidroga che controllava in treno mi ha disturbato molto mi ha sbavato addosso e violato la mia intimità !! Sono disgustata per questo!!! 1
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:48.510469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
529096 
not quoted
 
11373
quoted
 
6174

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 529096
96.8%
not quoted 11373
 
2.1%
quoted 6174
 
1.1%

Common Values (Plot)

2023-01-04T17:47:48.667412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_grund_beeintraecht_other_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct3726
Distinct (%)0.7%
Missing2245
Missing (%)0.4%
Memory size4.2 MiB
-66
537121 
-99
 
3381
Nichts
 
44
nichts
 
22
Rien
 
20
Other values (3721)
 
3810

Unique

Unique3673 ?
Unique (%)0.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 537121
98.3%
-99 3381
 
0.6%
Nichts 44
 
< 0.1%
nichts 22
 
< 0.1%
Rien 20
 
< 0.1%
rien 8
 
< 0.1%
Coronavirus 6
 
< 0.1%
Corona 6
 
< 0.1%
Ausländer 5
 
< 0.1%
Bettler 5
 
< 0.1%
Other values (3716) 3780
 
0.7%
(Missing) 2245
 
0.4%

OES_grund_personal_negativ_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct874
Distinct (%)0.2%
Missing109481
Missing (%)20.0%
Memory size4.2 MiB
-66
436176 
-99
 
111
Pas nécessaire
 
2
...
 
2
ACAB
 
2
Other values (869)
 
869

Unique

Unique869 ?
Unique (%)0.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 436176
79.8%
-99 111
 
< 0.1%
Pas nécessaire 2
 
< 0.1%
... 2
 
< 0.1%
ACAB 2
 
< 0.1%
Ich finde es sind zu wenig, wenn mich einer angreift geht es bestimmt 30 Sekunden oder mehr bis sie bei mir sind. Wer weiss, vtl geht es um Leben oder Tod 1
 
< 0.1%
fühle mich beobachtet 1
 
< 0.1%
Par expérience si j'avais eu un problème ils ne l auraient certainement pas résolu mais m'auraient envoyé à un poste. 1
 
< 0.1%
Weil sie martialisch tun. 1
 
< 0.1%
je n'ai jamais pris le train avec des forces de sécurité dedans sans que je sois controlé, c'est vraiment descriminatoire, j'ai toujours la sensation de représenter le symbole des profils ciblés par les forces de sécurité. Et c'est vraiment insupportable 1
 
< 0.1%
Other values (864) 864
 
0.2%
(Missing) 109481
 
20.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
411122 
-77
75717 
quoted
59804 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 411122
75.2%
-77 75717
 
13.9%
quoted 59804
 
10.9%

Common Values (Plot)

2023-01-04T17:47:48.779248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
73784 
-77
31168 
quoted
 
4527

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 73784
 
13.5%
-77 31168
 
5.7%
quoted 4527
 
0.8%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:48.897157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
quoted
334188 
not quoted
136738 
-77
75717 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rowquoted
3rd rowquoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 334188
61.1%
not quoted 136738
25.0%
-77 75717
 
13.9%

Common Values (Plot)

2023-01-04T17:47:49.016076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_personal_na
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
423842 
-77
75717 
quoted
47084 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 423842
77.5%
-77 75717
 
13.9%
quoted 47084
 
8.6%

Common Values (Plot)

2023-01-04T17:47:49.132345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
441831 
-77
75717 
quoted
 
29095

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 441831
80.8%
-77 75717
 
13.9%
quoted 29095
 
5.3%

Common Values (Plot)

2023-01-04T17:47:49.243365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
76818 
-77
31168 
quoted
 
1493

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 76818
 
14.1%
-77 31168
 
5.7%
quoted 1493
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:49.390061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct6
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
4
52920 
-77
31168 
not quoted
20654 
2
 
2363
3
 
1301

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row-77
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 52920
 
9.7%
-77 31168
 
5.7%
not quoted 20654
 
3.8%
2 2363
 
0.4%
3 1301
 
0.2%
quoted 1073
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:49.512597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

OES_personal_zug
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
459033 
-77
75717 
quoted
 
11893

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 459033
84.0%
-77 75717
 
13.9%
quoted 11893
 
2.2%

Common Values (Plot)

2023-01-04T17:47:49.645156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Ortskundigkeit
Categorical

Distinct7
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
31181 
3-12mal
27564 
nur dieses Mal
17131 
1-2mal
15483 
2-5mal pro Monat
9476 
Other values (2)
8644 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-5mal pro Monat
2nd row(fast) täglich
3rd row-77
4th row3-12mal
5th rowmehrmals pro Woche

Common Values

ValueCountFrequency (%)
-77 31181
 
5.7%
3-12mal 27564
 
5.0%
nur dieses Mal 17131
 
3.1%
1-2mal 15483
 
2.8%
2-5mal pro Monat 9476
 
1.7%
mehrmals pro Woche 5832
 
1.1%
(fast) täglich 2812
 
0.5%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:49.773767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

participant_id
Categorical

HIGH CARDINALITY  UNIQUE 

Distinct546643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
1
 
1
410231
 
1
410245
 
1
410244
 
1
410243
 
1
Other values (546638)
546638 

Unique

Unique546643 ?
Unique (%)100.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 1
 
< 0.1%
410231 1
 
< 0.1%
410245 1
 
< 0.1%
410244 1
 
< 0.1%
410243 1
 
< 0.1%
410242 1
 
< 0.1%
410241 1
 
< 0.1%
410240 1
 
< 0.1%
410239 1
 
< 0.1%
410238 1
 
< 0.1%
Other values (546633) 546633
> 99.9%

project
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-1
165697 
110723
162683 
212566
109481 
184113
69799 
174695
38983 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110723
2nd row110723
3rd row110723
4th row110723
5th row110723

Common Values

ValueCountFrequency (%)
-1 165697
30.3%
110723 162683
29.8%
212566 109481
20.0%
184113 69799
12.8%
174695 38983
 
7.1%

Common Values (Plot)

2023-01-04T17:47:49.933192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

projectLfn
Categorical

Distinct224271
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
881
 
5
32138
 
5
30965
 
5
30944
 
5
30888
 
5
Other values (224266)
546618 

Unique

Unique73932 ?
Unique (%)13.5%

Sample

1st row881
2nd row491
3rd row541
4th row502
5th row510

Common Values

ValueCountFrequency (%)
881 5
 
< 0.1%
32138 5
 
< 0.1%
30965 5
 
< 0.1%
30944 5
 
< 0.1%
30888 5
 
< 0.1%
30872 5
 
< 0.1%
30989 5
 
< 0.1%
30814 5
 
< 0.1%
32367 5
 
< 0.1%
30765 5
 
< 0.1%
Other values (224261) 546593
> 99.9%

R_abo_datum
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
159740 
1
 
5476
2
 
4268
3
 
2505
4
 
1700
Other values (8)
 
5591

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 159740
29.2%
1 5476
 
1.0%
2 4268
 
0.8%
3 2505
 
0.5%
4 1700
 
0.3%
5 1407
 
0.3%
6 1039
 
0.2%
7 835
 
0.2%
8 621
 
0.1%
11 594
 
0.1%
Other values (3) 1095
 
0.2%
(Missing) 367363
67.2%

R_abo_nutzung
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
157205 
Ja
19526 
Nein
 
2429
Weiss nicht
 
120

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 157205
28.8%
Ja 19526
 
3.6%
Nein 2429
 
0.4%
Weiss nicht 120
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:50.072993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_abotk_klasse
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
160336 
2. Klasse
 
15921
1. Klasse
 
2790
1. und 2. Klasse
 
233

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 160336
29.3%
2. Klasse 15921
 
2.9%
1. Klasse 2790
 
0.5%
1. und 2. Klasse 233
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:50.200108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_anschluss
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
276156 
Ja
256676 
Nein
 
13809
0
 
2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rowJa
5th row-77

Common Values

ValueCountFrequency (%)
-77 276156
50.5%
Ja 256676
47.0%
Nein 13809
 
2.5%
0 2
 
< 0.1%

Common Values (Plot)

2023-01-04T17:47:50.319103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_anschluss_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
nicht genannt
124305 
-77
51678 
genannt
 
3297

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt 124305
 
22.7%
-77 51678
 
9.5%
genannt 3297
 
0.6%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:50.436771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_anschluss_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
nicht genannt
125944 
-77
51678 
genannt
 
1658

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt 125944
 
23.0%
-77 51678
 
9.5%
genannt 1658
 
0.3%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:50.567124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_anschluss_3
Categorical

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
nicht genannt
125644 
-77
51678 
genannt
 
1958

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rownicht genannt
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
nicht genannt 125644
 
23.0%
-77 51678
 
9.5%
genannt 1958
 
0.4%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:50.760806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_fawkontrolle
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Nein
249555 
Ja
211217 
-77
66776 
Weiss nicht
 
18611
99
 
480
Other values (2)
 
4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein 249555
45.7%
Ja 211217
38.6%
-77 66776
 
12.2%
Weiss nicht 18611
 
3.4%
99 480
 
0.1%
0 2
 
< 0.1%
3 2
 
< 0.1%

Common Values (Plot)

2023-01-04T17:47:50.894984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
481370 
not quoted
61718 
quoted
 
3555

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 481370
88.1%
not quoted 61718
 
11.3%
quoted 3555
 
0.7%

Common Values (Plot)

2023-01-04T17:47:51.016096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_gastro_na
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
294530 
-77
244424 
quoted
 
7689

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted 294530
53.9%
-77 244424
44.7%
quoted 7689
 
1.4%

Common Values (Plot)

2023-01-04T17:47:51.120532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_gastro_nonuse
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
quoted
265138 
-77
244424 
not quoted
37081 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rowquoted
4th rowquoted
5th row-77

Common Values

ValueCountFrequency (%)
quoted 265138
48.5%
-77 244424
44.7%
not quoted 37081
 
6.8%

Common Values (Plot)

2023-01-04T17:47:51.229072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
290613 
-77
244424 
quoted
 
11606

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted 290613
53.2%
-77 244424
44.7%
quoted 11606
 
2.1%

Common Values (Plot)

2023-01-04T17:47:51.336903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
not quoted
287663 
-77
244424 
quoted
 
14556

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd rownot quoted
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
not quoted 287663
52.6%
-77 244424
44.7%
quoted 14556
 
2.7%

Common Values (Plot)

2023-01-04T17:47:51.447199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_1
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
4469
quoted
 
439

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 4469
 
0.8%
quoted 439
 
0.1%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:51.564582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_2
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
4738
quoted
 
170

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 4738
 
0.9%
quoted 170
 
< 0.1%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:51.689042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_3
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
4711
quoted
 
197

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 4711
 
0.9%
quoted 197
 
< 0.1%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:51.812472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_4
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
4165
quoted
 
743

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 4165
 
0.8%
quoted 743
 
0.1%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:51.939286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_5
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
2737
quoted
 
2171

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 2737
 
0.5%
quoted 2171
 
0.4%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:52.064731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_grund_nonuse_5txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct2080
Distinct (%)1.0%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-66
216092 
Krankheit
 
13
Zugausfall
 
12
Le train a été supprimé
 
6
Easy ride
 
5
Other values (2075)
 
2134

Unique

Unique2038 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 216092
39.5%
Krankheit 13
 
< 0.1%
Zugausfall 12
 
< 0.1%
Le train a été supprimé 6
 
< 0.1%
Easy ride 5
 
< 0.1%
Der Zug ist ausgefallen 5
 
< 0.1%
Klassenwechsel 4
 
< 0.1%
Zug ist ausgefallen 4
 
< 0.1%
Train supprimé 4
 
< 0.1%
Le train a été annulé 4
 
< 0.1%
Other values (2070) 2113
 
0.4%
(Missing) 328381
60.1%

R_grund_nonuse_6
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-77
213354 
not quouted
 
3448
quoted
 
1460

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 213354
39.0%
not quouted 3448
 
0.6%
quoted 1460
 
0.3%
(Missing) 328381
60.1%

Common Values (Plot)

2023-01-04T17:47:52.191751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_kb_wunsch
Categorical

Distinct5
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
0
63103 
Nein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst
54912 
-77
51676 
Nein, aber ich hätte mir die Präsenz einer Kundenbegleiterin oder eines Kundenbegleiters gewünscht
7568 
Ja, ich hätte gerne mit einer Kundenbegleiterin oder einem Kundenbegleiter gesprochen
 
2021

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd rowNein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
0 63103
 
11.5%
Nein, ich habe die Kundenbegleiterin/ den Kundenbegleiter nicht vermisst 54912
 
10.0%
-77 51676
 
9.5%
Nein, aber ich hätte mir die Präsenz einer Kundenbegleiterin oder eines Kundenbegleiters gewünscht 7568
 
1.4%
Ja, ich hätte gerne mit einer Kundenbegleiterin oder einem Kundenbegleiter gesprochen 2021
 
0.4%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:52.331144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)60.1%
Memory size4.2 MiB
Ja
173981 
-77
39526 
Nein
 
4756

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJa
2nd rowJa
3rd rowJa
4th rowJa
5th rowJa

Common Values

ValueCountFrequency (%)
Ja 173981
31.8%
-77 39526
 
7.2%
Nein 4756
 
0.9%
(Missing) 328380
60.1%

Common Values (Plot)

2023-01-04T17:47:52.492473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_nutzung_retour
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing328380
Missing (%)60.1%
Memory size4.2 MiB
-77
206915 
Ja, ich habe das Billett für die Hin- und Rückfahrt genutzt
 
11023
Nein, ich habe das Billett nur für die Hinfahrt genutzt
 
172
Nein, ich habe das Billett weder für die Hin- noch für die Rückfahrt genutzt
 
136
Nein, ich habe das Billett nur für die Rückfahrt genutzt
 
17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 206915
37.9%
Ja, ich habe das Billett für die Hin- und Rückfahrt genutzt 11023
 
2.0%
Nein, ich habe das Billett nur für die Hinfahrt genutzt 172
 
< 0.1%
Nein, ich habe das Billett weder für die Hin- noch für die Rückfahrt genutzt 136
 
< 0.1%
Nein, ich habe das Billett nur für die Rückfahrt genutzt 17
 
< 0.1%
(Missing) 328380
60.1%

Common Values (Plot)

2023-01-04T17:47:52.636847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_nutzung_tk
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
173177 
Ja
 
5858
Nein
 
245

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 173177
31.7%
Ja 5858
 
1.1%
Nein 245
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:47:52.798369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_Park
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct310
Distinct (%)0.3%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109163 
Zu teuer
 
4
Trop cher
 
3
Zu wenig Parkplätze
 
2
zu wenig Parkplätze
 
2
Other values (305)
 
305

Unique

Unique305 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109163
 
20.0%
Zu teuer 4
 
< 0.1%
Trop cher 3
 
< 0.1%
Zu wenig Parkplätze 2
 
< 0.1%
zu wenig Parkplätze 2
 
< 0.1%
Manque de places et prix trop élevés 1
 
< 0.1%
Durch den Umbau des gesamten Bahnhofs in Liestal ist der Weg vom Parkplatz zum Auto viel zu lang! Ich hoffe sehr, dass sich die Situation nach Vollendung des Neubaus bessert; Liestal ist ein Bahnhof, der von vielen Menschen mit dem Auto angefahren wird. Es ist wünschenswert, wenn es ein grosses Parkhaus in Bahnhofs-Nähe geben würde. 1
 
< 0.1%
Trop peu de places au P+R 1
 
< 0.1%
Die Kurzparkplätze Richtung Stadtzentrum wurden reduziert. Vielleicht wurden sie auch nur auf Grund der Baustelle vorübergehend entfernt. Dann wäre der Mangel schnell wieder behoben. 1
 
< 0.1%
Park und Ride war nicht verfügbar. Mussten im UNO Gebäude Parken 16:00-24:00 = CHF 45. Was absoluter Wucher ist. 1
 
< 0.1%
Other values (300) 300
 
0.1%
(Missing) 437164
80.0%

R_platz_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
3423
quoted
 
730

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 3423
 
0.6%
quoted 730
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:52.911870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_andere_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct680
Distinct (%)0.6%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
108749 
Baustelle
 
20
Travaux
 
11
Passerelle
 
5
Lift
 
4
Other values (675)
 
690

Unique

Unique663 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108749
 
19.9%
Baustelle 20
 
< 0.1%
Travaux 11
 
< 0.1%
Passerelle 5
 
< 0.1%
Lift 4
 
< 0.1%
Umbau 4
 
< 0.1%
Manque de bancs 3
 
< 0.1%
Chantier 2
 
< 0.1%
Treppen 2
 
< 0.1%
Überall zufrieden 2
 
< 0.1%
Other values (670) 677
 
0.1%
(Missing) 437164
80.0%

R_platz_gebauede
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
3457
quoted
 
696

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 3457
 
0.6%
quoted 696
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.016951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_perron_eng
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
2525
quoted
 
1628

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 2525
 
0.5%
quoted 1628
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.117316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_perron_leute
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
quoted
 
2393
not quoted
 
1760

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
quoted 2393
 
0.4%
not quoted 1760
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.219057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_unterf_eng
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
3105
quoted
 
1048

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 3105
 
0.6%
quoted 1048
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.320942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_unterf_leute
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
2210
quoted
 
1943

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 2210
 
0.4%
quoted 1943
 
0.4%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.422787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_vorbhf
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
3483
quoted
 
670

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 3483
 
0.6%
quoted 670
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.522607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_platz_warte
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105326 
not quoted
 
3681
quoted
 
472

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105326
 
19.3%
not quoted 3681
 
0.7%
quoted 472
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.620389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_anderes
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
not quoted
 
2823
quoted
 
315

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
not quoted 2823
 
0.5%
quoted 315
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.718531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_anderes_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct302
Distinct (%)0.3%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109164 
Lift
 
5
Baustelle
 
4
Treppen
 
4
Nirgends
 
2
Other values (297)
 
300

Unique

Unique294 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109164
 
20.0%
Lift 5
 
< 0.1%
Baustelle 4
 
< 0.1%
Treppen 4
 
< 0.1%
Nirgends 2
 
< 0.1%
Aucun 2
 
< 0.1%
Ovunque 2
 
< 0.1%
Zigarettenstummel überall 2
 
< 0.1%
La gare est en travaux les ouvriers doivent faire leur travail c’est temporaire 1
 
< 0.1%
Beim Abgang 1
 
< 0.1%
Other values (292) 292
 
0.1%
(Missing) 437164
80.0%

R_sauber_gebauede
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
not quoted
 
2296
quoted
 
842

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
not quoted 2296
 
0.4%
quoted 842
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.818944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_perron
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
quoted
 
1699
not quoted
 
1439

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
quoted 1699
 
0.3%
not quoted 1439
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:53.930353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_unterfuehrung
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
quoted
 
1848
not quoted
 
1290

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
quoted 1848
 
0.3%
not quoted 1290
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.035881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_vorbhf
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
not quoted
 
1981
quoted
 
1157

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
not quoted 1981
 
0.4%
quoted 1157
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.203714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_warte
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
not quoted
 
2307
quoted
 
831

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
not quoted 2307
 
0.4%
quoted 831
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.307636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_sauber_WC
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106341 
not quoted
 
2573
quoted
 
565

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 106341
 
19.5%
not quoted 2573
 
0.5%
quoted 565
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.412478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_Sharing
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109467 
Mobility is not available there
 
1
Il n'y avait plus qu'un seul vélo électrique à sa place, et étant donné que l'unique et dernier n'était pas à sa place initiale, la carte indiquait le vélo tel ????? Ce qui signifie que l'application n'arrivait pas à le situer juste. N'ayant plus de bus, les vélo devraient être plus en quantité et surtout électrique pour rentrer rapidement eb sécurité le soir.
 
1
Kein Mobility zur Verfügung. Über Auffahrt jedoch verständlich.
 
1
Üblicherweise gibt es vormittags zu wenige Publibikes in der Velostation Schanzenstrasse
 
1
Other values (8)
 
8

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109467
 
20.0%
Mobility is not available there 1
 
< 0.1%
Il n'y avait plus qu'un seul vélo électrique à sa place, et étant donné que l'unique et dernier n'était pas à sa place initiale, la carte indiquait le vélo tel ????? Ce qui signifie que l'application n'arrivait pas à le situer juste. N'ayant plus de bus, les vélo devraient être plus en quantité et surtout électrique pour rentrer rapidement eb sécurité le soir. 1
 
< 0.1%
Kein Mobility zur Verfügung. Über Auffahrt jedoch verständlich. 1
 
< 0.1%
Üblicherweise gibt es vormittags zu wenige Publibikes in der Velostation Schanzenstrasse 1
 
< 0.1%
Kein Service bei Rent-a-Bike. Entgegen Vetsprechen bei Ausgabe in Basel gab es kein Ersatzrad für defektes E- Bike. Ausserdem langes Anstehen bei Infoschalter für Radrückgabe. 1
 
< 0.1%
Es wäre wünschenswert, dass die SBB Sharingsprodukte via SBB App anbieten würde (inkl. Bezahlung) 1
 
< 0.1%
Schlechter Platz, war früher viel besser als noch direkt durch Unterführung zu erreichen 1
 
< 0.1%
- 1
 
< 0.1%
Pas de Kiosque magasin café à côté 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 437164
80.0%

R_stoerung
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Nein
432121 
-77
66778 
Ja
 
40807
Weiss nicht
 
6937

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNein
2nd rowNein
3rd rowNein
4th rowNein
5th rowNein

Common Values

ValueCountFrequency (%)
Nein 432121
79.0%
-77 66778
 
12.2%
Ja 40807
 
7.5%
Weiss nicht 6937
 
1.3%

Common Values (Plot)

2023-01-04T17:47:54.524604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_umsteig_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105292 
not quoted
 
2880
quoted
 
1307

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105292
 
19.3%
not quoted 2880
 
0.5%
quoted 1307
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.632587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_umsteig_andere_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct1256
Distinct (%)1.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
108172 
Baustelle
 
8
-
 
6
.
 
5
Nichts
 
5
Other values (1251)
 
1283

Unique

Unique1227 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108172
 
19.8%
Baustelle 8
 
< 0.1%
- 6
 
< 0.1%
. 5
 
< 0.1%
Nichts 5
 
< 0.1%
Umsteigezeit 4
 
< 0.1%
Travaux 3
 
< 0.1%
Alles ok 3
 
< 0.1%
nichts 3
 
< 0.1%
Verspätung 3
 
< 0.1%
Other values (1246) 1267
 
0.2%
(Missing) 437164
80.0%

R_umsteig_park
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105292 
not quoted
 
4004
quoted
 
183

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105292
 
19.3%
not quoted 4004
 
0.7%
quoted 183
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.738044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_umsteig_trambus
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105292 
not quoted
 
3368
quoted
 
819

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105292
 
19.3%
not quoted 3368
 
0.6%
quoted 819
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.848581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_umsteig_zug
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
105292 
quoted
 
2494
not quoted
 
1693

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 105292
 
19.3%
quoted 2494
 
0.5%
not quoted 1693
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:54.949907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_unzuf_comfort_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct16840
Distinct (%)3.1%
Missing11957
Missing (%)2.2%
Memory size4.2 MiB
-66
516077 
-99
 
1228
-
 
23
Altes Rollmaterial
 
20
Kein Sitzplatz
 
17
Other values (16835)
 
17321

Unique

Unique16621 ?
Unique (%)3.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 516077
94.4%
-99 1228
 
0.2%
- 23
 
< 0.1%
Altes Rollmaterial 20
 
< 0.1%
Kein Sitzplatz 17
 
< 0.1%
Überfüllt 15
 
< 0.1%
. 14
 
< 0.1%
Nein 13
 
< 0.1%
Zu voll 10
 
< 0.1%
Non 10
 
< 0.1%
Other values (16830) 17259
 
3.2%
(Missing) 11957
 
2.2%

R_unzuf_fahrplan_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct6690
Distinct (%)3.7%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-66
172521 
Verspätung
 
21
Zugausfall
 
6
Retard
 
5
Zug hatte Verspätung
 
4
Other values (6685)
 
6722

Unique

Unique6656 ?
Unique (%)3.7%

Sample

1st rowMagari fate dei sondaggi chiedendo informazioni piu logiche; avete chiesto l'ora di partenza ... che ne so io; quello che so e il ritardo al arrivo in minuti; usate il cervello e avrete piu risposte; evitate il tecnocratico che infetta le sbb
2nd row-66
3rd row-66
4th rowUmsteigen, zu volle Abteile, werde daher nicht mehr SBB benutzen, bevor die SBB mehr Abteile haben wird. Der Kondukteur könnte auch etwas freundlicher sein und den Gestapo Ton mal ablegen
5th row-66

Common Values

ValueCountFrequency (%)
-66 172521
31.6%
Verspätung 21
 
< 0.1%
Zugausfall 6
 
< 0.1%
Retard 5
 
< 0.1%
Zug hatte Verspätung 4
 
< 0.1%
Mehr Verbindungen 4
 
< 0.1%
Train en retard 4
 
< 0.1%
Zugsausfall 3
 
< 0.1%
30 min Verspätung 3
 
< 0.1%
Verspätung des Zuges 3
 
< 0.1%
Other values (6680) 6705
 
1.2%
(Missing) 367364
67.2%

R_unzuf_gastro_ambiente_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct156
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380792 
165697 
Tischtücher waren schmutzig
 
1
Etwas moderner werden und die Stoff Tischtücher weg nehmen sie sind manchmal nicht so hygienisch!
 
1
Il s'agissait d'un soit-disant bistro, un endroit terne avec de banquettes à partager avec d'autres voyageurs, très petit et peu accueillant.
 
1
Other values (151)
 
151

Unique

Unique154 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380792
69.7%
165697
30.3%
Tischtücher waren schmutzig 1
 
< 0.1%
Etwas moderner werden und die Stoff Tischtücher weg nehmen sie sind manchmal nicht so hygienisch! 1
 
< 0.1%
Il s'agissait d'un soit-disant bistro, un endroit terne avec de banquettes à partager avec d'autres voyageurs, très petit et peu accueillant. 1
 
< 0.1%
Es war ein Bombardier zug. Schlechte Einrichtung - kein Platz für Mäntel, 1
 
< 0.1%
Z. B saubere Tischtücher 1
 
< 0.1%
To busy and noise 1
 
< 0.1%
Eher unterkühlte Einrichtung. 1
 
< 0.1%
Die neuen Züge sind nicht mehr wirklich gemütlich (das trifft auf nahezu alle Züge von Stadler zu): Zu viele helle und glatte Flächen im Innenraum. Ich wünschte mir wärmere Farben und Texturen; weniger Glas, mehr Holz. Es ist zwar nett, dass so viele Infoscreens rumhängen, wenn ich aber sehe, wie Informationen auf diesen präsentiert werden, wundere ich mich, was hier die Überlegungen waren. So wird etwa die eingeblendete Uhr bei einer Durchsage vom Lautsprecher-Symbol verdeckt: Wiese befinden sich beide Symbole am selben Ort, wo sonst so viel Platz ist? Wies befindet sich die Uhr überhaupt unten links und nicht unten rechts: Dann bliebe viel mehr Platz für die Karte! Ausserdem, und dies ist viel wichtiger und stört mich schon seit langem extrem: Die Lautsprecher sind in fast allen Zügen viel, viel zu laut eingestellt. Es ist wirklich unangenehm bis schmerzhaft, wenn man direkt unter einem Lautsprecher sitzt. Und die Chance, dass man unter einem Lautsprecher sitzt, ist in den neuen Zügen sehr hoch. 1
 
< 0.1%
Other values (146) 146
 
< 0.1%

R_unzuf_gastro_auswahl_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380769 
165697 
Das Frühstücks-Angebot war unter aller Sau!
 
1
Die Hälfte der Gerichte auf der Karte war nicht verfügbar - es gab nur ein heisses Gericht
 
1
zuwenig Alternativen zur milch latte macciato mit Hafermilch ist nicht möglich, obwohl Hafermilch in der Speisekarte steht
 
1
Other values (174)
 
174

Unique

Unique177 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380769
69.7%
165697
30.3%
Das Frühstücks-Angebot war unter aller Sau! 1
 
< 0.1%
Die Hälfte der Gerichte auf der Karte war nicht verfügbar - es gab nur ein heisses Gericht 1
 
< 0.1%
zuwenig Alternativen zur milch latte macciato mit Hafermilch ist nicht möglich, obwohl Hafermilch in der Speisekarte steht 1
 
< 0.1%
Gerne Ein grosser Teil des Angebotes war nicht verfügbar (kein Frühstück, keine Gipfeli, etc.), zudem war die Kaffeemaschine erst nach 45 Minuten Fahrt einsatzbereit. Angegeben wurden logistische Gründe, ein einmaliges Malheur sozusagen. Von 10 Malen, die ich seit September auf dieser Verbindung und Uhrzeit den Spesewagen genutzt habe, ist das 3 Mal vorgekommen. Es scheint mir mehr als ein Versehen zu sein, sondern an Ihrer Logistik. Anregung: wahrscheinlich früher aufstehen:). Scherz beiseite: irgend eine Form von Sicherheit einbauen. Das schaffen Sie bestimmt. 1
 
< 0.1%
Als Person die sich vegetarisch ernährt und eine Glutenunverträglichkeit hat, gibt es kaum Auswahl. Ich fahre 2x pro Woche diese Strecke, habe ich mich schon mehrfach ohne Erfolg dazu geäußert. Vielleicht hilft es diesmal. Nicht auf dieserReise aber auf anderen wurde mir klar, dass das Personal nicht geschult Ist und den Unterschied zwischen vegan und glutenfrei nicht kennt. Es ollte meiner Meinung nach zumindest glutenfreies Brot geben. Im Winter schafft man es mit Risotto, wenn dieses vegetarisch ist aber seit Monaten wird mir Bulgur (nicht glutenfrei) oder Ravioli (nicht glutenfrei) angeboten. Schade 1
 
< 0.1%
keine Gipfeli Wir (eine mir unbekannte Passagierin und ich) mussten mehrmals nachfragen um die Rg. zu begleichen obwohl nicht Grossandrang war. 1
 
< 0.1%
Nur ein paar Kägi Kekse verfügbar. Kaffee viel teurer als im ICE und winzig. 1
 
< 0.1%
kein darvida nichts gesundes 1
 
< 0.1%
Other values (169) 169
 
< 0.1%
Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380899 
165697 
Sehr unfreundliche Bedienug!
 
1
Der Herr im Bistro, hat uns in Thun aus dem Bistro verabschiedet. Er müsse nun abrechnen und bis Bern aufräumen, da dieses Bistro anschliessend geschlossen werde. Entsprechend standen wir anschliessend mit 10 anderen Personen vor dem WC, resp. auf der Treppe...
 
1
Zeigte null Interesse und vergass beinahe das Einkassieren…ich hätte beibahe deb Zug zu spät verlassen
 
1
Other values (44)
 
44

Unique

Unique47 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380899
69.7%
165697
30.3%
Sehr unfreundliche Bedienug! 1
 
< 0.1%
Der Herr im Bistro, hat uns in Thun aus dem Bistro verabschiedet. Er müsse nun abrechnen und bis Bern aufräumen, da dieses Bistro anschliessend geschlossen werde. Entsprechend standen wir anschliessend mit 10 anderen Personen vor dem WC, resp. auf der Treppe... 1
 
< 0.1%
Zeigte null Interesse und vergass beinahe das Einkassieren…ich hätte beibahe deb Zug zu spät verlassen 1
 
< 0.1%
Die Bedienung war sehr unfreundlich 1
 
< 0.1%
Ein Lächeln bringt ein Lächeln zurück 1
 
< 0.1%
Es war ein Bistro der Deutschen Bahn... Die Service-Mitarbeiterin war sehr wortkarg (kein Danke, kein Bitte etc.). 1
 
< 0.1%
They forced my companion to order, and didn’t give him enough time to decide on what he wanted. 1
 
< 0.1%
Ich fand die Person ziemlich unfreundlich. An jeder Station sind Leute eingestiegen, welche eine Reservation für den Speisewagen hatte. Der Mann, hat dann einfach ziemlich gehässig die Anzahl Personen aufgefordert Platz zu machen. 1
 
< 0.1%
Other values (39) 39
 
< 0.1%

R_unzuf_gastro_kompetenz_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380877 
165697 
Es ging viel zu lange bis ich das Essen bekam . Hatte nur noch 10 Minuten Zeit zum essen. Habe vorab gefragt ob die Zeit reiche. Dies wurde mir versichert. Schlechte Beratung dadurch und das Fleisch war nicht warm
 
1
Die Dame war überaus unhöflich, inkompetent, motzig und unsymphatisch.
 
1
Konnte nicht online zahlen. Wurde auch nicht vorher kommuniziert
 
1
Other values (66)
 
66

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380877
69.7%
165697
30.3%
Es ging viel zu lange bis ich das Essen bekam . Hatte nur noch 10 Minuten Zeit zum essen. Habe vorab gefragt ob die Zeit reiche. Dies wurde mir versichert. Schlechte Beratung dadurch und das Fleisch war nicht warm 1
 
< 0.1%
Die Dame war überaus unhöflich, inkompetent, motzig und unsymphatisch. 1
 
< 0.1%
Konnte nicht online zahlen. Wurde auch nicht vorher kommuniziert 1
 
< 0.1%
Der Herr war freundlich, angenehm und sympathisch, aber eher langsam und teilweise etwas überfordert. Für uns war das aber kein Problem. 1
 
< 0.1%
Zuerst musste das Personal noch alles einrichten und Tischtücher montieren, bevor wir endlich fast in Winterthur etwas bestellen konnten.... 1
 
< 0.1%
La personne présente était très aimable et de bonne volonté . Mais manifestement ne semblait pas vraiment formée pour ce travail. Je me suis dit que vous deviez avoir des problèmes pour recruter des personnes qualifiées dans ce domaine . 1
 
< 0.1%
Der Kellner erschien erstmals etwa drei Minuten nach Abfahrt des Zuges bei der Küche, wo er den Rolladen betätigte. Im Speisewagen waren fast alle Plätze belegt. Ich wurde dann im Hauenstein-Tunnel nach meinen Wünschen befragt. 1
 
< 0.1%
Gennerell das Personal…… 1
 
< 0.1%
Other values (61) 61
 
< 0.1%

R_unzuf_gastro_preis_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct268
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380664 
165697 
Zu teuer
 
12
Überteuert
 
3
Troppo caro
 
2
Other values (263)
 
265

Unique

Unique261 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380664
69.6%
165697
30.3%
Zu teuer 12
 
< 0.1%
Überteuert 3
 
< 0.1%
Troppo caro 2
 
< 0.1%
Viel zu teuer 2
 
< 0.1%
Relativ teuer 2
 
< 0.1%
Bisschen günstiger für die Qualität und mehr Personal, da Wartezeit zu lang 1
 
< 0.1%
Einige Sachen gingen günstiger. 1
 
< 0.1%
Die Getränke sind eindeutig zu teuer. 1
 
< 0.1%
Other values (258) 258
 
< 0.1%

R_unzuf_info_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct3427
Distinct (%)1.6%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-66
214826 
Ich war zufrieden
 
3
-
 
3
Ich war zufrieden!
 
3
Maskenpflicht
 
2
Other values (3422)
 
3425

Unique

Unique3419 ?
Unique (%)1.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th rowLes 10 chf que j ai payé correspondent à deux réservations vélo (2 vélos) . Néanmoins je n ai pas pu réserver les places vélos que 1 jour avant . Avant cela pas de possibilité de réserver . Même la hotline m affirme qu ‘ il n était pas possible de réserver deux places pour vélos pour cause de week end de Pentecôte , information qui s’est révélée fausse
5th row-66

Common Values

ValueCountFrequency (%)
-66 214826
39.3%
Ich war zufrieden 3
 
< 0.1%
- 3
 
< 0.1%
Ich war zufrieden! 3
 
< 0.1%
Maskenpflicht 2
 
< 0.1%
sauberkeit 2
 
< 0.1%
? 2
 
< 0.1%
Zu teuer. 2
 
< 0.1%
J'ai imprimer le billet que j'ai trouvé peu clair. Je suis allé sur le site billets dégriffés mais je n'ai vu sur le billet aucune indication pour savoir si il s'agit bien d'un billet dégriffé ou non. Les informations allées-retour sont à mon sens difficiles à déchiffrés. Pour avoir des renseignements complémentaires j'ai écrit un email (num. de référence CS000512064) via le site internet cff.ch. le 25.05 à 10:05. J'ai reçu un courriel automatique de service@SBB.ch me garantissant une réponse dans les 48 heures. J'attends toujours ... (28.05.2021 / 15:06). 1
 
< 0.1%
Teilweise werden für jeden unterschiedliche Verbindungen für verschiedene Personen angezeigt, nervig für die Planung mit Freunden 1
 
< 0.1%
Other values (3417) 3417
 
0.6%
(Missing) 328381
60.1%

R_unzuf_mobile_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct5672
Distinct (%)3.2%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-66
173388 
Schlechter Empfang
 
19
Kein Empfang
 
18
Kein WLAN
 
17
Kein wlan
 
14
Other values (5667)
 
5823

Unique

Unique5586 ?
Unique (%)3.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 173388
31.7%
Schlechter Empfang 19
 
< 0.1%
Kein Empfang 18
 
< 0.1%
Kein WLAN 17
 
< 0.1%
Kein wlan 14
 
< 0.1%
- 14
 
< 0.1%
Nein 13
 
< 0.1%
Kein WLAN im Zug 8
 
< 0.1%
Kein Wlan 6
 
< 0.1%
No 5
 
< 0.1%
Other values (5662) 5777
 
1.1%
(Missing) 367364
67.2%

R_unzuf_platzangebot_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct9636
Distinct (%)4.4%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-66
208230 
Zu wenig Sitzplätze
 
21
Überfüllt
 
21
Zug war überfüllt
 
15
Der Zug war sehr voll.
 
15
Other values (9631)
 
9960

Unique

Unique9481 ?
Unique (%)4.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 208230
38.1%
Zu wenig Sitzplätze 21
 
< 0.1%
Überfüllt 21
 
< 0.1%
Zug war überfüllt 15
 
< 0.1%
Der Zug war sehr voll. 15
 
< 0.1%
Train bondé 10
 
< 0.1%
Trop de monde 9
 
< 0.1%
Der Zug war sehr voll 8
 
< 0.1%
Beaucoup de monde 8
 
< 0.1%
Zu viele Leute 8
 
< 0.1%
Other values (9626) 9917
 
1.8%
(Missing) 328381
60.1%

R_unzuf_preis_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct13736
Distinct (%)7.7%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-66
164791 
Zu teuer
 
180
Trop cher
 
88
zu teuer
 
67
Teuer
 
22
Other values (13731)
 
14131

Unique

Unique13576 ?
Unique (%)7.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 164791
30.1%
Zu teuer 180
 
< 0.1%
Trop cher 88
 
< 0.1%
zu teuer 67
 
< 0.1%
Teuer 22
 
< 0.1%
Viel zu teuer 22
 
< 0.1%
Sehr teuer 19
 
< 0.1%
trop cher 17
 
< 0.1%
Prix trop élevé 13
 
< 0.1%
- 12
 
< 0.1%
Other values (13726) 14048
 
2.6%
(Missing) 367364
67.2%

R_unzuf_gastro_quality_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
380848 
165697 
Kaffee war kalt
 
2
le chocolat chaud était froid!
 
1
Speisewagen war nicht bedient. Wir kamen von einem verspäteten Flug und hätten gerne etwas gegessen und getrunken. Das ist innert 6 Monaten das 2. Mal, dass mir das passiert.
 
1
Other values (94)
 
94

Unique

Unique96 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 380848
69.7%
165697
30.3%
Kaffee war kalt 2
 
< 0.1%
le chocolat chaud était froid! 1
 
< 0.1%
Speisewagen war nicht bedient. Wir kamen von einem verspäteten Flug und hätten gerne etwas gegessen und getrunken. Das ist innert 6 Monaten das 2. Mal, dass mir das passiert. 1
 
< 0.1%
zwei Cappuccino bestellt dessen Schaum nicht ein leichter Milchschaum war sondern eine sehr solide steife Masse die mehr als die Hälfte des Inhalts ausmachte. Anscheinend in der Maschine so vorprogrammiert aber da müsste man was ändern, es war kein Genuss. Wenn man denn mal zum Kaffee kam, war der gut. 1
 
< 0.1%
No options only nuts were available 1
 
< 0.1%
Le café expresso n'est pas terrible du tout, vous pourriez faire mieux, surtout avec l'offre très variée de capsules. 1
 
< 0.1%
il ny avais pas de croissant et rien d'autre pour déjeuner pour un voyage de 4 heure via Lugano j'ai pris des boisson le service Top 1
 
< 0.1%
Das Gipfeli war trocken. 1
 
< 0.1%
Other values (89) 89
 
< 0.1%

R_unzuf_puenktlichkeit_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct6005
Distinct (%)2.8%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-66
211946 
Verspätung
 
81
Retard
 
33
Zug hatte Verspätung
 
11
Der Zug hatte Verspätung
 
11
Other values (6000)
 
6180

Unique

Unique5909 ?
Unique (%)2.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 211946
38.8%
Verspätung 81
 
< 0.1%
Retard 33
 
< 0.1%
Zug hatte Verspätung 11
 
< 0.1%
Der Zug hatte Verspätung 11
 
< 0.1%
Train en retard 11
 
< 0.1%
Retard du train 10
 
< 0.1%
Anschluss verpasst 10
 
< 0.1%
Zugausfall 9
 
< 0.1%
Zugsausfall 8
 
< 0.1%
Other values (5995) 6132
 
1.1%
(Missing) 328381
60.1%

R_unzuf_Sauberkeit_Bhf_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct2556
Distinct (%)2.3%
Missing437861
Missing (%)80.1%
Memory size4.2 MiB
-66
106193 
-
 
8
Baustelle
 
6
Abfall am Boden
 
5
.
 
4
Other values (2551)
 
2566

Unique

Unique2539 ?
Unique (%)2.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 106193
 
19.4%
- 8
 
< 0.1%
Baustelle 6
 
< 0.1%
Abfall am Boden 5
 
< 0.1%
. 4
 
< 0.1%
Littering 4
 
< 0.1%
Travaux 3
 
< 0.1%
Dreckig 2
 
< 0.1%
Überall Müll 2
 
< 0.1%
Déchets au sol 2
 
< 0.1%
Other values (2546) 2553
 
0.5%
(Missing) 437861
80.1%

R_unzuf_sicherheit_zug
Categorical

IMBALANCE  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109445 
Keine
 
2
je ne pense pas que les controles doivent se faire en présence des forces de sécurité
 
1
C'est surprenant et dommage d'avoir besoin de forces de sécurité dans le train...
 
1
Si la polizia Svizzera non è presente sul treno.
 
1
Other values (29)
 
29

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109445
 
20.0%
Keine 2
 
< 0.1%
je ne pense pas que les controles doivent se faire en présence des forces de sécurité 1
 
< 0.1%
C'est surprenant et dommage d'avoir besoin de forces de sécurité dans le train... 1
 
< 0.1%
Si la polizia Svizzera non è presente sul treno. 1
 
< 0.1%
Elles sont inutiles et augmentent artificiellement le prix des billets. 1
 
< 0.1%
Jeden Tag dazu lernen 1
 
< 0.1%
this is the second time that there are loud and truly drunk passengers who disrupt other passengers. in this case between between Lausanne and Yverdon. Last time was between Lausanne and Auvernier 1
 
< 0.1%
Zivilpolizisten, die nicht gerade freundlich mit Menschen mit Migrationshintergrund umgingen. 1
 
< 0.1%
Vier Sicherheitskräfte standen plaudernd bei den Türen am einen Ende des Waggons während auf der anderen Seite Leute angepöbelt wurden und der Treppenaufgang von der 1.Kl. unten zur Türe mit sitzenden Hockeyfans blockiert wurde. Einer dieser Fans war sogar maskiert. 1
 
< 0.1%
Other values (24) 24
 
< 0.1%
(Missing) 437164
80.0%

R_unzuf_stoerungsinfo_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct3767
Distinct (%)1.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-66
324115 
-99
 
469
Non
 
4
aucune information
 
4
Es gab keine Information
 
4
Other values (3762)
 
3784

Unique

Unique3746 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 324115
59.3%
-99 469
 
0.1%
Non 4
 
< 0.1%
aucune information 4
 
< 0.1%
Es gab keine Information 4
 
< 0.1%
Es gab keine Informationen 4
 
< 0.1%
Besser informieren 3
 
< 0.1%
Siehe oben 3
 
< 0.1%
3
 
< 0.1%
Nein 3
 
< 0.1%
Other values (3757) 3768
 
0.7%
(Missing) 218263
39.9%

R_unzuf_wc_avail_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct6103
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
539434 
-99
 
709
Kein WC vorhanden
 
22
Defekt
 
21
WC war defekt
 
12
Other values (6098)
 
6445

Unique

Unique5913 ?
Unique (%)1.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 539434
98.7%
-99 709
 
0.1%
Kein WC vorhanden 22
 
< 0.1%
Defekt 21
 
< 0.1%
WC war defekt 12
 
< 0.1%
WC war geschlossen 11
 
< 0.1%
War geschlossen 10
 
< 0.1%
WC defekt 10
 
< 0.1%
Nein 9
 
< 0.1%
Es gibt kein WC 8
 
< 0.1%
Other values (6093) 6397
 
1.2%

R_unzuf_wc_clean_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct4607
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
541394 
-99
 
502
Putzen
 
8
Pas propre
 
7
-
 
7
Other values (4602)
 
4725

Unique

Unique4529 ?
Unique (%)0.8%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 541394
99.0%
-99 502
 
0.1%
Putzen 8
 
< 0.1%
Pas propre 7
 
< 0.1%
- 7
 
< 0.1%
Dreckig 7
 
< 0.1%
Schmutzig 7
 
< 0.1%
Es war nicht sauber 5
 
< 0.1%
nicht sauber 5
 
< 0.1%
Putzen! 5
 
< 0.1%
Other values (4597) 4696
 
0.9%

R_unzuf_Wegweisung_Bhf_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct2420
Distinct (%)2.2%
Missing437861
Missing (%)80.1%
Memory size4.2 MiB
-66
106322 
Baustelle
 
10
.
 
8
Nein
 
5
Bauarbeiten
 
5
Other values (2415)
 
2432

Unique

Unique2404 ?
Unique (%)2.2%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 106322
 
19.4%
Baustelle 10
 
< 0.1%
. 8
 
< 0.1%
Nein 5
 
< 0.1%
Bauarbeiten 5
 
< 0.1%
Umbau 4
 
< 0.1%
Idem 3
 
< 0.1%
- 3
 
< 0.1%
Travaux 3
 
< 0.1%
Unübersichtlich 3
 
< 0.1%
Other values (2410) 2416
 
0.4%
(Missing) 437861
80.1%

R_unzuf_zug_clean_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct11326
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
533755 
-99
 
1245
.
 
15
Dreckige Sitze
 
13
Nein
 
13
Other values (11321)
 
11602

Unique

Unique11191 ?
Unique (%)2.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 533755
97.6%
-99 1245
 
0.2%
. 15
 
< 0.1%
Dreckige Sitze 13
 
< 0.1%
Nein 13
 
< 0.1%
- 13
 
< 0.1%
Schmutzige Sitze 13
 
< 0.1%
Putzen 10
 
< 0.1%
9
 
< 0.1%
Schmutzig 9
 
< 0.1%
Other values (11316) 11548
 
2.1%

R_unzuf_zugpers_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct3210
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
543168 
-99
 
214
Unfreundlich
 
17
Nein
 
6
unfreundlich
 
4
Other values (3205)
 
3234

Unique

Unique3181 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 543168
99.4%
-99 214
 
< 0.1%
Unfreundlich 17
 
< 0.1%
Nein 6
 
< 0.1%
unfreundlich 4
 
< 0.1%
Keine Kontrolle 3
 
< 0.1%
Siehe vorher 3
 
< 0.1%
Ich habe kein Zugpersonal gesehen 3
 
< 0.1%
No 3
 
< 0.1%
Habe kein Zugpersonal gesehen 3
 
< 0.1%
Other values (3200) 3219
 
0.6%

R_Velo
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct680
Distinct (%)0.6%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
108785 
Zu wenige
 
5
Zu wenige Abstellplätze
 
4
Zu wenig
 
3
Es hat zu wenig.
 
2
Other values (675)
 
680

Unique

Unique670 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108785
 
19.9%
Zu wenige 5
 
< 0.1%
Zu wenige Abstellplätze 4
 
< 0.1%
Zu wenig 3
 
< 0.1%
Es hat zu wenig. 2
 
< 0.1%
Pas assez de place 2
 
< 0.1%
zu wenige 2
 
< 0.1%
Zu wenig Platz 2
 
< 0.1%
Überfüllt 2
 
< 0.1%
zu wenig Plätze 2
 
< 0.1%
Other values (670) 670
 
0.1%
(Missing) 437164
80.0%

R_WC
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct120
Distinct (%)0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109360 
Alte stinkige Anlage. Ein Umbau wäre dringend nötig.
 
1
Keine Möglichkeit mit Karte oder TWINT zu bezahlen. Kein Münzwechsler in der Nähe.
 
1
Für Personen ohne Handy, gibt es heute noch bei älteren Personen. Die haben keine Chance.
 
1
Man kann nur aufs WC mit einer ec oder Kreditkarte, das finde ich unglaublich!!!!! Kinder und Jugendliche, Erwachsene und ältere Menschen besitzen keine oder haben die Karte nicht immer dabei!!!!!!
 
1
Other values (115)
 
115

Unique

Unique119 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109360
 
20.0%
Alte stinkige Anlage. Ein Umbau wäre dringend nötig. 1
 
< 0.1%
Keine Möglichkeit mit Karte oder TWINT zu bezahlen. Kein Münzwechsler in der Nähe. 1
 
< 0.1%
Für Personen ohne Handy, gibt es heute noch bei älteren Personen. Die haben keine Chance. 1
 
< 0.1%
Man kann nur aufs WC mit einer ec oder Kreditkarte, das finde ich unglaublich!!!!! Kinder und Jugendliche, Erwachsene und ältere Menschen besitzen keine oder haben die Karte nicht immer dabei!!!!!! 1
 
< 0.1%
Die WC sind SEHR alt und SEHR schmutzig 1
 
< 0.1%
Im WC bei den Schliessfächern waren die Männer und Damentoiletten leider ziemlich verschmutzt. 1
 
< 0.1%
Die WC's waren richtig dreckig! Bei kostenpflichtigen WC's erwarte ich tadellose Sauberkeit. 1
 
< 0.1%
Nicht sauber - es wird scheinbar nur Geld (1€) verlangt und nichts in die Sauberkeit und Infrastruktur investiert. 1
 
< 0.1%
Die Anlage ist veraltet! 1
 
< 0.1%
Other values (110) 110
 
< 0.1%
(Missing) 437164
80.0%

R_wc_na_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
78155 
-77
31158 
quoted
 
166

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 78155
 
14.3%
-77 31158
 
5.7%
quoted 166
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.079927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_na_ziel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
78207 
-77
31158 
quoted
 
114

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 78207
 
14.3%
-77 31158
 
5.7%
quoted 114
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.192281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_na_zug
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
77107 
-77
31158 
quoted
 
1214

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 77107
 
14.1%
-77 31158
 
5.7%
quoted 1214
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.309779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_nutzung
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Nein, hatte keinen Bedarf
404082 
-77
66756 
Ja
64754 
Nein, war nicht verfügbar
 
10570
4
 
480

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNein, hatte keinen Bedarf
2nd rowNein, war nicht verfügbar
3rd rowNein, hatte keinen Bedarf
4th rowNein, hatte keinen Bedarf
5th rowNein, hatte keinen Bedarf

Common Values

ValueCountFrequency (%)
Nein, hatte keinen Bedarf 404082
73.9%
-77 66756
 
12.2%
Ja 64754
 
11.8%
Nein, war nicht verfügbar 10570
 
1.9%
4 480
 
0.1%
0 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:47:55.426681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_start
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
77643 
-77
31158 
quoted
 
678

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 77643
 
14.2%
-77 31158
 
5.7%
quoted 678
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.554730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_ziel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
77650 
-77
31158 
quoted
 
671

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 77650
 
14.2%
-77 31158
 
5.7%
quoted 671
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.666338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_wc_zug
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
65131 
-77
31158 
quoted
13190 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 65131
 
11.9%
-77 31158
 
5.7%
quoted 13190
 
2.4%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.781145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
1889
quoted
 
748

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 1889
 
0.3%
quoted 748
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:55.893417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_andere_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct714
Distinct (%)0.7%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
108731 
Baustelle
 
10
Ausgang
 
6
Keine
 
5
Schliessfächer
 
4
Other values (709)
 
723

Unique

Unique700 ?
Unique (%)0.6%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108731
 
19.9%
Baustelle 10
 
< 0.1%
Ausgang 6
 
< 0.1%
Keine 5
 
< 0.1%
Schliessfächer 4
 
< 0.1%
. 3
 
< 0.1%
Ausgänge 3
 
< 0.1%
Rien 3
 
< 0.1%
Zum Lift 3
 
< 0.1%
Sortie 3
 
< 0.1%
Other values (704) 708
 
0.1%
(Missing) 437164
80.0%

R_weg_laeden
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
2359
quoted
 
278

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 2359
 
0.4%
quoted 278
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.003220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_park
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
2473
quoted
 
164

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 2473
 
0.5%
quoted 164
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.111608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_share
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
2520
quoted
 
117

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 2520
 
0.5%
quoted 117
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.220095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_trambus
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
1855
quoted
 
782

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 1855
 
0.3%
quoted 782
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.329284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_velo
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
2499
quoted
 
138

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 2499
 
0.5%
quoted 138
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.432398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_WC
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
2160
quoted
 
477

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 2160
 
0.4%
quoted 477
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.533168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_weg_zug
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106842 
not quoted
 
1339
quoted
 
1298

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 106842
 
19.5%
not quoted 1339
 
0.2%
quoted 1298
 
0.2%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.630518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

R_zweck
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Freizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
115837 
Freizeitfahrt/ private Ferienreise/ alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abhol
75943 
Geschäftsreise
70060 
Private Ferienreise (Reise mit mind. 1 Übernachtung)
69106 
Freizeitfahrt/ private Ferienreise
67042 
Other values (9)
148655 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
2nd rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
3rd rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)
4th rowPrivate Ferienreise (Reise mit mind. 1 Übernachtung)
5th rowFreizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.)

Common Values

ValueCountFrequency (%)
Freizeitfahrt ohne Übernachtung (Ausflug, Kino, Sport, Besuch, usw.) 115837
21.2%
Freizeitfahrt/ private Ferienreise/ alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abhol 75943
13.9%
Geschäftsreise 70060
12.8%
Private Ferienreise (Reise mit mind. 1 Übernachtung) 69106
12.6%
Freizeitfahrt/ private Ferienreise 67042
12.3%
Fahrt zum Arbeitsort 40771
 
7.5%
Fahrt vom oder zum Arbeits-/ Ausbildungsort 31849
 
5.8%
Alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. abholen) 26770
 
4.9%
Fahrt zum Ausbildungsort 20426
 
3.7%
alltägliche Erledigungen (z.B. Arztbesuch, Einkaufen, jmd. Abholen) 10300
 
1.9%
Other values (4) 18539
 
3.4%

RF_bhf_abfahrt
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
101223 
quoted
 
5156
not quoted
 
3100

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 101223
 
18.5%
quoted 5156
 
0.9%
not quoted 3100
 
0.6%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.731585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_bhf_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
101223 
not quoted
 
8074
quoted
 
182

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 101223
 
18.5%
not quoted 8074
 
1.5%
quoted 182
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.832393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_bhf_perron
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
101223 
quoted
 
6866
not quoted
 
1390

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 101223
 
18.5%
quoted 6866
 
1.3%
not quoted 1390
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:56.944945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_bhf_touch
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
101223 
not quoted
 
8055
quoted
 
201

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 101223
 
18.5%
not quoted 8055
 
1.5%
quoted 201
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:57.058733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
quoted
146853 
not quoted
56209 
-77
44269 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd rownot quoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 146853
26.9%
not quoted 56209
 
10.3%
-77 44269
 
8.1%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.168882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_12
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
172284 
-77
44269 
quoted
30778 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 172284
31.5%
-77 44269
 
8.1%
quoted 30778
 
5.6%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.283836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
168380 
-77
44269 
quoted
34682 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 168380
30.8%
-77 44269
 
8.1%
quoted 34682
 
6.3%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.397974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_14
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
199954 
-77
44269 
quoted
 
3108

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 199954
36.6%
-77 44269
 
8.1%
quoted 3108
 
0.6%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.507130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_15
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
not quoted
61345 
-77
38322 
quoted
9812 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 61345
 
11.2%
-77 38322
 
7.0%
quoted 9812
 
1.8%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:57.675047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
183542 
-77
44269 
quoted
19520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rowquoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 183542
33.6%
-77 44269
 
8.1%
quoted 19520
 
3.6%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.777078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_4
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing408792
Missing (%)74.8%
Memory size4.2 MiB
not quoted
124021 
quoted
 
7883
-77
 
5947

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 124021
 
22.7%
quoted 7883
 
1.4%
-77 5947
 
1.1%
(Missing) 408792
74.8%

Common Values (Plot)

2023-01-04T17:47:57.884876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_6
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
200864 
-77
44269 
quoted
 
2198

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 200864
36.7%
-77 44269
 
8.1%
quoted 2198
 
0.4%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:57.990674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
183772 
-77
44269 
quoted
19290 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 183772
33.6%
-77 44269
 
8.1%
quoted 19290
 
3.5%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:58.097889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
149530 
quoted
53532 
-77
44269 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rowquoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 149530
27.4%
quoted 53532
 
9.8%
-77 44269
 
8.1%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:58.207934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_keiner
Categorical

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
181639 
-77
44269 
quoted
21423 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 181639
33.2%
-77 44269
 
8.1%
quoted 21423
 
3.9%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:58.322806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_other
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
not quoted
200172 
-77
44269 
quoted
 
2890

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 200172
36.6%
-77 44269
 
8.1%
quoted 2890
 
0.5%
(Missing) 299312
54.8%

Common Values (Plot)

2023-01-04T17:47:58.435972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_kanal_other_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct2299
Distinct (%)0.9%
Missing299312
Missing (%)54.8%
Memory size4.2 MiB
-66
244441 
Google maps
 
93
Google Maps
 
84
google maps
 
36
ZVV App
 
25
Other values (2294)
 
2652

Unique

Unique2169 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 244441
44.7%
Google maps 93
 
< 0.1%
Google Maps 84
 
< 0.1%
google maps 36
 
< 0.1%
ZVV App 25
 
< 0.1%
Google 17
 
< 0.1%
Internet 16
 
< 0.1%
Google map 14
 
< 0.1%
DB App 12
 
< 0.1%
Anzeige im Bus 11
 
< 0.1%
Other values (2289) 2582
 
0.5%
(Missing) 299312
54.8%

RF_mob_andere
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
74670 
not quoted
34065 
quoted
 
744

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 74670
 
13.7%
not quoted 34065
 
6.2%
quoted 744
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:58.551643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_mob_andere_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct483
Distinct (%)0.4%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
108735 
Fahrplan
 
39
Easy Ride
 
24
EasyRide
 
16
Easy ride
 
15
Other values (478)
 
650

Unique

Unique422 ?
Unique (%)0.4%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 108735
 
19.9%
Fahrplan 39
 
< 0.1%
Easy Ride 24
 
< 0.1%
EasyRide 16
 
< 0.1%
Easy ride 15
 
< 0.1%
Ticketkauf 15
 
< 0.1%
Achat billet 12
 
< 0.1%
Achat du billet 11
 
< 0.1%
easy ride 10
 
< 0.1%
Billetkauf 8
 
< 0.1%
Other values (473) 594
 
0.1%
(Missing) 437164
80.0%

RF_mob_autom
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
74670 
not quoted
29348 
quoted
 
5461

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 74670
 
13.7%
not quoted 29348
 
5.4%
quoted 5461
 
1.0%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:58.668733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_mob_fahrplan
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
74670 
quoted
32337 
not quoted
 
2472

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd row-77
4th row-77
5th rowquoted

Common Values

ValueCountFrequency (%)
-77 74670
 
13.7%
quoted 32337
 
5.9%
not quoted 2472
 
0.5%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:58.786209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_mob_karte
Categorical

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
74670 
not quoted
33293 
quoted
 
1516

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd row-77
4th row-77
5th rownot quoted

Common Values

ValueCountFrequency (%)
-77 74670
 
13.7%
not quoted 33293
 
6.1%
quoted 1516
 
0.3%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:58.898266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_webs_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106486 
not quoted
 
2920
quoted
 
73

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-77 106486
 
19.5%
not quoted 2920
 
0.5%
quoted 73
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:59.002492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_webs_andere_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct72
Distinct (%)0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109406 
Achat du billet
 
2
Ticket
 
2
Buy tickets
 
1
SBB App
 
1
Other values (67)
 
67

Unique

Unique69 ?
Unique (%)0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109406
 
20.0%
Achat du billet 2
 
< 0.1%
Ticket 2
 
< 0.1%
Buy tickets 1
 
< 0.1%
SBB App 1
 
< 0.1%
Réservation billet 1
 
< 0.1%
den abnormalen Zustand im Bahnhof Liestal / vor Antritt der Reise vor Ort informiert, um die Weiterfahrt mit dem Bus rechtzeitig zu finden. 1
 
< 0.1%
Every stop 1
 
< 0.1%
Gruppenreise 1
 
< 0.1%
Easy-ride 1
 
< 0.1%
Other values (62) 62
 
< 0.1%
(Missing) 437164
80.0%

RF_webs_erwsuche
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106486 
not quoted
 
2637
quoted
 
356

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-77 106486
 
19.5%
not quoted 2637
 
0.5%
quoted 356
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:59.102784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_webs_fahrplan
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106486 
quoted
 
2802
not quoted
 
191

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rowquoted
5th row-77

Common Values

ValueCountFrequency (%)
-77 106486
 
19.5%
quoted 2802
 
0.5%
not quoted 191
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:59.205077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_webs_karte
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
106486 
not quoted
 
2856
quoted
 
137

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th rownot quoted
5th row-77

Common Values

ValueCountFrequency (%)
-77 106486
 
19.5%
not quoted 2856
 
0.5%
quoted 137
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:47:59.306319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

RF_Zufallsitem1
Categorical

Distinct21
Distinct (%)< 0.1%
Missing227748
Missing (%)41.7%
Memory size4.2 MiB
SBB Mobile App
114011 
-66
52035 
1
34853 
-77
21480 
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
21210 
Other values (16)
75306 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBB Mobile App
2nd rowWebseite sbb.ch
3rd rowSBB Mobile App
4th rowSBB Mobile App
5th rowSBB Mobile App

Common Values

ValueCountFrequency (%)
SBB Mobile App 114011
20.9%
-66 52035
 
9.5%
1 34853
 
6.4%
-77 21480
 
3.9%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore) 21210
 
3.9%
Webseite sbb.ch 19395
 
3.5%
Anzeigen im Zug 9127
 
1.7%
6 8262
 
1.5%
Durchsagen im Zug 7802
 
1.4%
8 5971
 
1.1%
Other values (11) 24749
 
4.5%
(Missing) 227748
41.7%

RF_zufallsitem1_label
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct696
Distinct (%)0.3%
Missing301740
Missing (%)55.2%
Memory size4.2 MiB
-66
66157 
SBB Mobile App
50260 
SBB Mobile App (Smartphone)
28903 
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
14980 
Appli Mobile CFF
10510 
Other values (691)
74093 

Unique

Unique599 ?
Unique (%)0.2%

Sample

1st rowSBB Mobile App (Smartphone)
2nd rowAnnonces dans le train
3rd rowAppli Mobile CFF (smartphone)
4th rowAnzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
5th rowSBB Mobile App (Smartphone)

Common Values

ValueCountFrequency (%)
-66 66157
 
12.1%
SBB Mobile App 50260
 
9.2%
SBB Mobile App (Smartphone) 28903
 
5.3%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore) 14980
 
2.7%
Appli Mobile CFF 10510
 
1.9%
Anzeigen im Zug 9300
 
1.7%
Webseite sbb.ch 7967
 
1.5%
Durchsagen im Zug 7616
 
1.4%
Appli Mobile CFF (smartphone) 5971
 
1.1%
Affichages en gare (p. ex. affichages des départs, écrans d’affichage) 5177
 
0.9%
Other values (686) 38062
 
7.0%
(Missing) 301740
55.2%

S_AB1_GA2kl
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
not quoted
290685 
-77
 
28826
quoted
 
8869

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot quoted
2nd rownot quoted
3rd rownot quoted
4th rownot quoted
5th rownot quoted

Common Values

ValueCountFrequency (%)
not quoted 290685
53.2%
-77 28826
 
5.3%
quoted 8869
 
1.6%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:59.412419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB2_GA
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing328380
Missing (%)60.1%
Memory size4.2 MiB
nicht genannt
185289 
genannt
24693 
-77
 
8281

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rowgenannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt 185289
33.9%
genannt 24693
 
4.5%
-77 8281
 
1.5%
(Missing) 328380
60.1%

Common Values (Plot)

2023-01-04T17:47:59.528510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB2_GA1kl
Categorical

Distinct3
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
178285 
nicht genannt
149621 
genannt
 
474

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
-77 178285
32.6%
nicht genannt 149621
27.4%
genannt 474
 
0.1%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:47:59.642328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB3_HTA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
quoted
417778 
not quoted
91758 
-77
 
37107

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquoted
2nd rowquoted
3rd rowquoted
4th rowquoted
5th rowquoted

Common Values

ValueCountFrequency (%)
quoted 417778
76.4%
not quoted 91758
 
16.8%
-77 37107
 
6.8%

Common Values (Plot)

2023-01-04T17:47:59.754018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB4
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
nicht genannt
491998 
-77
 
37107
genannt
 
17538

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt 491998
90.0%
-77 37107
 
6.8%
genannt 17538
 
3.2%

Common Values (Plot)

2023-01-04T17:47:59.857425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB5
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
nicht genannt
463807 
genannt
 
45729
-77
 
37107

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rowgenannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt 463807
84.8%
genannt 45729
 
8.4%
-77 37107
 
6.8%

Common Values (Plot)

2023-01-04T17:47:59.964731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
503730 
nicht genannt
 
38036
genannt
 
4877

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 503730
92.1%
nicht genannt 38036
 
7.0%
genannt 4877
 
0.9%

Common Values (Plot)

2023-01-04T17:48:00.073550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
nicht genannt
494327 
-77
 
37107
genannt
 
15209

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt 494327
90.4%
-77 37107
 
6.8%
genannt 15209
 
2.8%

Common Values (Plot)

2023-01-04T17:48:00.180470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_AB7txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct4849
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
386646 
-99
145837 
Juniorkarte
 
2995
Juniorkarten
 
580
Carte junior
 
263
Other values (4844)
 
10322

Unique

Unique4155 ?
Unique (%)0.8%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 386646
70.7%
-99 145837
 
26.7%
Juniorkarte 2995
 
0.5%
Juniorkarten 580
 
0.1%
Carte junior 263
 
< 0.1%
Junior 261
 
< 0.1%
Enkelkarte 240
 
< 0.1%
juniorkarte 176
 
< 0.1%
FVP-GA 151
 
< 0.1%
Velo GA 150
 
< 0.1%
Other values (4839) 9344
 
1.7%

S_AB8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
nicht genannt
463044 
genannt
46492 
-77
 
37107

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownicht genannt
2nd rownicht genannt
3rd rownicht genannt
4th rownicht genannt
5th rownicht genannt

Common Values

ValueCountFrequency (%)
nicht genannt 463044
84.7%
genannt 46492
 
8.5%
-77 37107
 
6.8%

Common Values (Plot)

2023-01-04T17:48:00.288333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_alter
Categorical

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
45666 
50
 
14218
55
 
13140
60
 
12242
52
 
12069
Other values (86)
449308 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 45666
 
8.4%
50 14218
 
2.6%
55 13140
 
2.4%
60 12242
 
2.2%
52 12069
 
2.2%
53 11609
 
2.1%
54 11424
 
2.1%
56 11411
 
2.1%
57 11257
 
2.1%
58 11089
 
2.0%
Other values (81) 392518
71.8%
Distinct3
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
ja
115372 
nein
56539 
-77
 
7369

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownein
2nd rownein
3rd rowja
4th rowja
5th rownein

Common Values

ValueCountFrequency (%)
ja 115372
 
21.1%
nein 56539
 
10.3%
-77 7369
 
1.3%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:48:00.415010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_sex
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
weiblich
277987 
männlich
259366 
-77
 
8280
divers
 
1007
0
 
3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowweiblich
2nd rowweiblich
3rd rowweiblich
4th rowweiblich
5th rowmännlich

Common Values

ValueCountFrequency (%)
weiblich 277987
50.9%
männlich 259366
47.4%
-77 8280
 
1.5%
divers 1007
 
0.2%
0 3
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:00.548229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_sprache
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Deutsch
393335 
Français
101553 
English
 
31504
Italiano
 
20229
0
 
21

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDeutsch
2nd rowDeutsch
3rd rowFrançais
4th rowDeutsch
5th rowDeutsch

Common Values

ValueCountFrequency (%)
Deutsch 393335
72.0%
Français 101553
 
18.6%
English 31504
 
5.8%
Italiano 20229
 
3.7%
0 21
 
< 0.1%
-77 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:00.685732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_Usertyp1
Categorical

Distinct8
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
63882 
mehrmals pro Woche
26080 
nie
23972 
3-12mal jährlich
19919 
(fast) täglich
19041 
Other values (3)
26386 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row2-5mal pro Monat
4th rownie
5th row-77

Common Values

ValueCountFrequency (%)
-77 63882
 
11.7%
mehrmals pro Woche 26080
 
4.8%
nie 23972
 
4.4%
3-12mal jährlich 19919
 
3.6%
(fast) täglich 19041
 
3.5%
2-5mal pro Monat 18449
 
3.4%
1-2mal jährlich 7933
 
1.5%
0 4
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:48:00.852655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_Usertyp2
Categorical

Distinct7
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
122738 
2-5mal pro Monat
22380 
3-12mal jährlich
20062 
mehrmals pro Woche
 
10761
(fast) täglich
 
1887
Other values (2)
 
1452

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownie
2nd row3-12mal jährlich
3rd row-77
4th row-77
5th row1-2mal jährlich

Common Values

ValueCountFrequency (%)
-77 122738
 
22.5%
2-5mal pro Monat 22380
 
4.1%
3-12mal jährlich 20062
 
3.7%
mehrmals pro Woche 10761
 
2.0%
(fast) täglich 1887
 
0.3%
1-2mal jährlich 1240
 
0.2%
nie 212
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:48:01.092980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_wohnsitz
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
In der Schweiz / Liechtenstein
512542 
In einem anderen Land
 
25820
-77
 
8280
0
 
1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIn der Schweiz / Liechtenstein
2nd rowIn der Schweiz / Liechtenstein
3rd rowIn der Schweiz / Liechtenstein
4th rowIn der Schweiz / Liechtenstein
5th rowIn der Schweiz / Liechtenstein

Common Values

ValueCountFrequency (%)
In der Schweiz / Liechtenstein 512542
93.8%
In einem anderen Land 25820
 
4.7%
-77 8280
 
1.5%
0 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:01.227557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

S_Usertyp3
Categorical

Distinct8
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
-77
63906 
2-5mal pro Monat
42729 
3-12mal jährlich
42595 
mehrmals pro Woche
15607 
1-2mal jährlich
6623 
Other values (3)
7820 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row2-5mal pro Monat
4th row1-2mal jährlich
5th row-77

Common Values

ValueCountFrequency (%)
-77 63906
 
11.7%
2-5mal pro Monat 42729
 
7.8%
3-12mal jährlich 42595
 
7.8%
mehrmals pro Woche 15607
 
2.9%
1-2mal jährlich 6623
 
1.2%
nie 4209
 
0.8%
(fast) täglich 3609
 
0.7%
0 2
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:48:01.366145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_1_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
361159 
10
 
1519
8
 
1005
9
 
782
7
 
657
Other values (7)
 
2236

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 361159
66.1%
10 1519
 
0.3%
8 1005
 
0.2%
9 782
 
0.1%
7 657
 
0.1%
5 652
 
0.1%
6 429
 
0.1%
4 394
 
0.1%
3 305
 
0.1%
1 233
 
< 0.1%
Other values (2) 223
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_12_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
354829 
10
 
2762
8
 
2116
9
 
1387
5
 
1316
Other values (7)
 
4948

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 354829
64.9%
10 2762
 
0.5%
8 2116
 
0.4%
9 1387
 
0.3%
5 1316
 
0.2%
7 1300
 
0.2%
6 873
 
0.2%
4 779
 
0.1%
1 755
 
0.1%
3 715
 
0.1%
Other values (2) 526
 
0.1%
(Missing) 179285
32.8%

SF_kanal_13_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
366150 
10
 
229
8
 
173
9
 
141
5
 
128
Other values (7)
 
537

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 366150
67.0%
10 229
 
< 0.1%
8 173
 
< 0.1%
9 141
 
< 0.1%
5 128
 
< 0.1%
7 119
 
< 0.1%
4 94
 
< 0.1%
6 86
 
< 0.1%
1 84
 
< 0.1%
3 76
 
< 0.1%
Other values (2) 78
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_14_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
365325 
10
 
608
8
 
304
9
 
231
5
 
189
Other values (7)
 
701

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 365325
66.8%
10 608
 
0.1%
8 304
 
0.1%
9 231
 
< 0.1%
5 189
 
< 0.1%
7 169
 
< 0.1%
1 122
 
< 0.1%
6 117
 
< 0.1%
4 93
 
< 0.1%
3 90
 
< 0.1%
Other values (2) 110
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_2_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
366827 
10
 
122
9
 
86
8
 
85
7
 
59
Other values (7)
 
179

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 366827
67.1%
10 122
 
< 0.1%
9 86
 
< 0.1%
8 85
 
< 0.1%
7 59
 
< 0.1%
5 50
 
< 0.1%
6 36
 
< 0.1%
3 25
 
< 0.1%
4 23
 
< 0.1%
1 18
 
< 0.1%
Other values (2) 27
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_4_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
366580 
10
 
228
8
 
96
5
 
92
9
 
90
Other values (7)
 
272

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 366580
67.1%
10 228
 
< 0.1%
8 96
 
< 0.1%
5 92
 
< 0.1%
9 90
 
< 0.1%
7 82
 
< 0.1%
4 50
 
< 0.1%
weiss nicht 38
 
< 0.1%
6 36
 
< 0.1%
3 26
 
< 0.1%
Other values (2) 40
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_6_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
366748 
10
 
166
5
 
74
9
 
69
8
 
61
Other values (7)
 
240

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 366748
67.1%
10 166
 
< 0.1%
5 74
 
< 0.1%
9 69
 
< 0.1%
8 61
 
< 0.1%
7 48
 
< 0.1%
1 46
 
< 0.1%
6 38
 
< 0.1%
3 34
 
< 0.1%
weiss nicht 26
 
< 0.1%
Other values (2) 48
 
< 0.1%
(Missing) 179285
32.8%

SF_kanal_7_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
359252 
10
 
1766
8
 
1347
5
 
934
9
 
899
Other values (7)
 
3160

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 359252
65.7%
10 1766
 
0.3%
8 1347
 
0.2%
5 934
 
0.2%
9 899
 
0.2%
7 853
 
0.2%
6 585
 
0.1%
4 479
 
0.1%
1 464
 
0.1%
3 422
 
0.1%
Other values (2) 357
 
0.1%
(Missing) 179285
32.8%

SF_kanal_8_zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
358641 
10
 
2096
8
 
1576
9
 
1028
7
 
972
Other values (7)
 
3045

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 358641
65.6%
10 2096
 
0.4%
8 1576
 
0.3%
9 1028
 
0.2%
7 972
 
0.2%
5 888
 
0.2%
6 580
 
0.1%
4 467
 
0.1%
1 427
 
0.1%
3 382
 
0.1%
Other values (2) 301
 
0.1%
(Missing) 179285
32.8%

SF_kanal_other_zuf
Categorical

IMBALANCE  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
365333 
1
 
589
weiss nicht
 
268
10
 
234
5
 
170
Other values (8)
 
764

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 365333
66.8%
1 589
 
0.1%
weiss nicht 268
 
< 0.1%
10 234
 
< 0.1%
5 170
 
< 0.1%
8 149
 
< 0.1%
3 131
 
< 0.1%
2 112
 
< 0.1%
7 108
 
< 0.1%
6 92
 
< 0.1%
Other values (3) 172
 
< 0.1%
(Missing) 179285
32.8%

RF_kanal_1_Zuf
Categorical

Distinct12
Distinct (%)< 0.1%
Missing463585
Missing (%)84.8%
Memory size4.2 MiB
5
52001 
4
15297 
10
 
4460
3
 
3008
weiss nicht
 
2511
Other values (7)
5781 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row8
3rd row10
4th row10
5th row9

Common Values

ValueCountFrequency (%)
5 52001
 
9.5%
4 15297
 
2.8%
10 4460
 
0.8%
3 3008
 
0.6%
weiss nicht 2511
 
0.5%
9 1823
 
0.3%
8 1387
 
0.3%
2 914
 
0.2%
1 798
 
0.1%
7 613
 
0.1%
Other values (2) 246
 
< 0.1%
(Missing) 463585
84.8%

RF_kanal_2_Zuf
Categorical

Distinct12
Distinct (%)0.1%
Missing537352
Missing (%)98.3%
Memory size4.2 MiB
5
4882 
4
1776 
10
714 
3
 
467
weiss nicht
 
386
Other values (7)
1066 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row8

Common Values

ValueCountFrequency (%)
5 4882
 
0.9%
4 1776
 
0.3%
10 714
 
0.1%
3 467
 
0.1%
weiss nicht 386
 
0.1%
9 346
 
0.1%
8 277
 
0.1%
2 150
 
< 0.1%
7 116
 
< 0.1%
1 114
 
< 0.1%
Other values (2) 63
 
< 0.1%
(Missing) 537352
98.3%

RF_kanal_4_Zuf
Categorical

Distinct11
Distinct (%)0.4%
Missing543761
Missing (%)99.5%
Memory size4.2 MiB
5
1564 
4
581 
10
167 
3
157 
weiss nicht
 
141
Other values (6)
272 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row7
4th row10
5th row3

Common Values

ValueCountFrequency (%)
5 1564
 
0.3%
4 581
 
0.1%
10 167
 
< 0.1%
3 157
 
< 0.1%
weiss nicht 141
 
< 0.1%
9 71
 
< 0.1%
8 66
 
< 0.1%
2 51
 
< 0.1%
1 39
 
< 0.1%
7 34
 
< 0.1%
(Missing) 543761
99.5%

RF_kanal_6_Zuf
Categorical

Distinct12
Distinct (%)1.7%
Missing545920
Missing (%)99.9%
Memory size4.2 MiB
5
373 
4
120 
weiss nicht
59 
3
41 
1
40 
Other values (7)
90 

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row9
2nd row10
3rd row5
4th row10
5th row10

Common Values

ValueCountFrequency (%)
5 373
 
0.1%
4 120
 
< 0.1%
weiss nicht 59
 
< 0.1%
3 41
 
< 0.1%
1 40
 
< 0.1%
10 34
 
< 0.1%
2 26
 
< 0.1%
9 17
 
< 0.1%
8 5
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
(Missing) 545920
99.9%

RF_kanal_7_Zuf
Categorical

Distinct12
Distinct (%)0.2%
Missing541656
Missing (%)99.1%
Memory size4.2 MiB
5
2482 
4
1364 
3
390 
weiss nicht
 
241
2
 
113
Other values (7)
397 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row9
4th row6
5th row7

Common Values

ValueCountFrequency (%)
5 2482
 
0.5%
4 1364
 
0.2%
3 390
 
0.1%
weiss nicht 241
 
< 0.1%
2 113
 
< 0.1%
1 112
 
< 0.1%
10 95
 
< 0.1%
8 60
 
< 0.1%
9 49
 
< 0.1%
7 48
 
< 0.1%
Other values (2) 33
 
< 0.1%
(Missing) 541656
99.1%

RF_kanal_8_Zuf
Categorical

IMBALANCE  MISSING 

Distinct14
Distinct (%)0.1%
Missing528956
Missing (%)96.8%
Memory size4.2 MiB
5
10630 
4
4056 
3
 
747
10
 
669
weiss nicht
 
359
Other values (9)
1226 

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row10
4th row10
5th row8

Common Values

ValueCountFrequency (%)
5 10630
 
1.9%
4 4056
 
0.7%
3 747
 
0.1%
10 669
 
0.1%
weiss nicht 359
 
0.1%
9 311
 
0.1%
8 305
 
0.1%
2 249
 
< 0.1%
1 170
 
< 0.1%
7 132
 
< 0.1%
Other values (4) 59
 
< 0.1%
(Missing) 528956
96.8%

RF_kanal_12_Zuf
Categorical

Distinct12
Distinct (%)0.1%
Missing537123
Missing (%)98.3%
Memory size4.2 MiB
5
5319 
4
2565 
3
578 
weiss nicht
 
384
10
 
175
Other values (7)
 
499

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row8
3rd row7
4th row8
5th row10

Common Values

ValueCountFrequency (%)
5 5319
 
1.0%
4 2565
 
0.5%
3 578
 
0.1%
weiss nicht 384
 
0.1%
10 175
 
< 0.1%
2 169
 
< 0.1%
8 94
 
< 0.1%
1 91
 
< 0.1%
9 78
 
< 0.1%
7 48
 
< 0.1%
Other values (2) 19
 
< 0.1%
(Missing) 537123
98.3%

RF_kanal_13_Zuf
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)0.1%
Missing535744
Missing (%)98.0%
Memory size4.2 MiB
5
6652 
4
2621 
3
 
506
weiss nicht
 
380
10
 
270
Other values (7)
 
470

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row9
4th row10
5th row10

Common Values

ValueCountFrequency (%)
5 6652
 
1.2%
4 2621
 
0.5%
3 506
 
0.1%
weiss nicht 380
 
0.1%
10 270
 
< 0.1%
8 119
 
< 0.1%
2 109
 
< 0.1%
9 105
 
< 0.1%
1 67
 
< 0.1%
7 41
 
< 0.1%
Other values (2) 29
 
< 0.1%
(Missing) 535744
98.0%

RF_kanal_14_Zuf
Categorical

IMBALANCE  MISSING 

Distinct11
Distinct (%)0.3%
Missing543242
Missing (%)99.4%
Memory size4.2 MiB
5
2028 
4
832 
weiss nicht
219 
3
 
185
2
 
47
Other values (6)
 
90

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10
2nd row2
3rd rowweiss nicht
4th row10
5th row7

Common Values

ValueCountFrequency (%)
5 2028
 
0.4%
4 832
 
0.2%
weiss nicht 219
 
< 0.1%
3 185
 
< 0.1%
2 47
 
< 0.1%
10 33
 
< 0.1%
1 26
 
< 0.1%
9 13
 
< 0.1%
8 9
 
< 0.1%
7 8
 
< 0.1%
(Missing) 543242
99.4%

RF_kanal_24_Zuf
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546643
Missing (%)100.0%
Memory size4.2 MiB

SF_bhf_abfahrt
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108383 
not quoted
 
756
quoted
 
340

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108383
 
19.8%
not quoted 756
 
0.1%
quoted 340
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:01.527482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_bhf_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108383 
not quoted
 
1053
quoted
 
43

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108383
 
19.8%
not quoted 1053
 
0.2%
quoted 43
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:01.627203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_bhf_perron
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108383 
quoted
 
850
not quoted
 
246

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108383
 
19.8%
quoted 850
 
0.2%
not quoted 246
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:01.727750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_bhf_stoerung
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108383 
not quoted
 
988
quoted
 
108

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108383
 
19.8%
not quoted 988
 
0.2%
quoted 108
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:01.830434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_bhf_touch
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108383 
not quoted
 
1089
quoted
 
7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108383
 
19.8%
not quoted 1089
 
0.2%
quoted 7
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:01.930998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
512605 
quoted
 
19807
not quoted
 
14231

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 512605
93.8%
quoted 19807
 
3.6%
not quoted 14231
 
2.6%

Common Values (Plot)

2023-01-04T17:48:02.031662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
512605 
quoted
 
19322
not quoted
 
14716

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 512605
93.8%
quoted 19322
 
3.5%
not quoted 14716
 
2.7%

Common Values (Plot)

2023-01-04T17:48:02.131626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_3
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
512754 
not quoted
 
27883
quoted
 
6006

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 512754
93.8%
not quoted 27883
 
5.1%
quoted 6006
 
1.1%

Common Values (Plot)

2023-01-04T17:48:02.240503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_4
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing179285
Missing (%)32.8%
Memory size4.2 MiB
-77
367209 
not quoted
 
124
quoted
 
25

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 367209
67.2%
not quoted 124
 
< 0.1%
quoted 25
 
< 0.1%
(Missing) 179285
32.8%

Common Values (Plot)

2023-01-04T17:48:02.353569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_5
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
512605 
not quoted
 
21907
quoted
 
12131

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 512605
93.8%
not quoted 21907
 
4.0%
quoted 12131
 
2.2%

Common Values (Plot)

2023-01-04T17:48:02.464578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
512605 
not quoted
 
19551
quoted
 
14487

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 512605
93.8%
not quoted 19551
 
3.6%
quoted 14487
 
2.7%

Common Values (Plot)

2023-01-04T17:48:02.563303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_info_art_7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
527309 
quoted
 
10147
not quoted
 
9187

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 527309
96.5%
quoted 10147
 
1.9%
not quoted 9187
 
1.7%

Common Values (Plot)

2023-01-04T17:48:02.662486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
506011 
not quoted
 
31210
quoted
 
9422

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506011
92.6%
not quoted 31210
 
5.7%
quoted 9422
 
1.7%

Common Values (Plot)

2023-01-04T17:48:02.761224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_12
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
506011 
not quoted
 
23722
quoted
 
16910

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506011
92.6%
not quoted 23722
 
4.3%
quoted 16910
 
3.1%

Common Values (Plot)

2023-01-04T17:48:02.860877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_13
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
506011 
not quoted
 
38817
quoted
 
1815

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506011
92.6%
not quoted 38817
 
7.1%
quoted 1815
 
0.3%

Common Values (Plot)

2023-01-04T17:48:02.968192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_14
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-77
506011 
not quoted
 
37899
quoted
 
2733

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506011
92.6%
not quoted 37899
 
6.9%
quoted 2733
 
0.5%

Common Values (Plot)

2023-01-04T17:48:03.073406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_15
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
102324 
not quoted
 
6904
quoted
 
251

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 102324
 
18.7%
not quoted 6904
 
1.3%
quoted 251
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:03.180178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_2
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
39836
quoted
 
796

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 39836
 
7.3%
quoted 796
 
0.1%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.286049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_4
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing109481
Missing (%)20.0%
Memory size4.2 MiB
-77
403685 
not quoted
 
32419
quoted
 
1058

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 403685
73.8%
not quoted 32419
 
5.9%
quoted 1058
 
0.2%
(Missing) 109481
 
20.0%

Common Values (Plot)

2023-01-04T17:48:03.399217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
39824
quoted
 
808

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 39824
 
7.3%
quoted 808
 
0.1%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.503067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
29352
quoted
 
11280

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 29352
 
5.4%
quoted 11280
 
2.1%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.604391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
28913
quoted
 
11719

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 28913
 
5.3%
quoted 11719
 
2.1%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.707203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_keiner
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
37478
quoted
 
3154

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 37478
 
6.9%
quoted 3154
 
0.6%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.810109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_other
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-77
506010 
not quoted
 
37891
quoted
 
2741

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 506010
92.6%
not quoted 37891
 
6.9%
quoted 2741
 
0.5%
(Missing) 1
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:03.912955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_kanal_other_txt
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct1788
Distinct (%)0.3%
Missing863
Missing (%)0.2%
Memory size4.2 MiB
-66
531079 
-99
 
12833
Verspätung
 
30
Radio
 
10
Retard
 
6
Other values (1783)
 
1822

Unique

Unique1761 ?
Unique (%)0.3%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 531079
97.2%
-99 12833
 
2.3%
Verspätung 30
 
< 0.1%
Radio 10
 
< 0.1%
Retard 6
 
< 0.1%
Andere Fahrgäste 6
 
< 0.1%
Durchsage im Zug 6
 
< 0.1%
Mitreisende 5
 
< 0.1%
Ersatzzug 5
 
< 0.1%
Zugverspätung 3
 
< 0.1%
Other values (1778) 1797
 
0.3%
(Missing) 863
 
0.2%

SF_mob_andere
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108211 
not quoted
 
1229
quoted
 
39

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108211
 
19.8%
not quoted 1229
 
0.2%
quoted 39
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:04.012914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_mob_andere_txt
Categorical

IMBALANCE  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-66
109440 
Fahrplan
 
4
Ticketkauf
 
3
keine
 
2
Haben Info auf dem Bahnhof bekommen
 
1
Other values (29)
 
29

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 109440
 
20.0%
Fahrplan 4
 
< 0.1%
Ticketkauf 3
 
< 0.1%
keine 2
 
< 0.1%
Haben Info auf dem Bahnhof bekommen 1
 
< 0.1%
Billette lösen 1
 
< 0.1%
Fahrplan geschaut 1
 
< 0.1%
Achat de billets 1
 
< 0.1%
Sbb app ohne push Meldung 1
 
< 0.1%
Ticket-Kauf 1
 
< 0.1%
Other values (24) 24
 
< 0.1%
(Missing) 437164
80.0%

SF_mob_autom
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108211 
not quoted
 
883
quoted
 
385

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108211
 
19.8%
not quoted 883
 
0.2%
quoted 385
 
0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:04.114759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_mob_fahrplan
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108211 
quoted
 
1113
not quoted
 
155

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108211
 
19.8%
quoted 1113
 
0.2%
not quoted 155
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:04.215759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_mob_karte
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing437164
Missing (%)80.0%
Memory size4.2 MiB
-77
108211 
not quoted
 
1209
quoted
 
59

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 108211
 
19.8%
not quoted 1209
 
0.2%
quoted 59
 
< 0.1%
(Missing) 437164
80.0%

Common Values (Plot)

2023-01-04T17:48:04.324101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

tag_zug_nr
Categorical

Distinct416276
Distinct (%)76.2%
Missing96
Missing (%)< 0.1%
Memory size4.2 MiB
2022-08-25;
 
194
2020-06-09;
 
180
2020-07-20;
 
167
2022-07-14;
 
162
2022-08-16;
 
145
Other values (416271)
545699 

Unique

Unique370026 ?
Unique (%)67.7%

Sample

1st row2018-07-01;12
2nd row2018-07-01;170
3rd row2018-07-01;5
4th row2018-07-01;8
5th row2018-07-01;8

Common Values

ValueCountFrequency (%)
2022-08-25; 194
 
< 0.1%
2020-06-09; 180
 
< 0.1%
2020-07-20; 167
 
< 0.1%
2022-07-14; 162
 
< 0.1%
2022-08-16; 145
 
< 0.1%
2022-08-11; 143
 
< 0.1%
2022-09-24; 137
 
< 0.1%
2022-08-10; 134
 
< 0.1%
2022-08-04; 132
 
< 0.1%
2022-08-13; 130
 
< 0.1%
Other values (416266) 545023
99.7%

u_artikel
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct247
Distinct (%)0.1%
Missing165697
Missing (%)30.3%
Memory size4.2 MiB
125
160645 
-66
77272 
52508
 
15319
52509
 
10657
10972
 
8614
Other values (242)
108439 

Unique

Unique40 ?
Unique (%)< 0.1%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
125 160645
29.4%
-66 77272
14.1%
52508 15319
 
2.8%
52509 10657
 
1.9%
10972 8614
 
1.6%
2007 7032
 
1.3%
7725 6483
 
1.2%
4092 6316
 
1.2%
12444 6187
 
1.1%
12448 5423
 
1.0%
Other values (237) 76998
14.1%
(Missing) 165697
30.3%

u_bezugsart
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
-66
191868 
Mobile
186923 
MobileTicket
48320 
Internet
45946 
Mobile Ticket
34706 
Other values (2)
38880 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
-66 191868
35.1%
Mobile 186923
34.2%
MobileTicket 48320
 
8.8%
Internet 45946
 
8.4%
Mobile Ticket 34706
 
6.3%
Onlineticket 22642
 
4.1%
-99 16238
 
3.0%

Common Values (Plot)

2023-01-04T17:48:04.452285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_unzuf_info_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct1511
Distinct (%)0.7%
Missing328381
Missing (%)60.1%
Memory size4.2 MiB
-66
216741 
.
 
4
Zu späte Information
 
3
siehe vorne
 
2
-
 
2
Other values (1506)
 
1510

Unique

Unique1502 ?
Unique (%)0.7%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 216741
39.6%
. 4
 
< 0.1%
Zu späte Information 3
 
< 0.1%
siehe vorne 2
 
< 0.1%
- 2
 
< 0.1%
Kam zu spät 2
 
< 0.1%
Manque de précision 2
 
< 0.1%
???? 2
 
< 0.1%
Siehe oben 2
 
< 0.1%
Perche non è stata data nessuna alternativa ai passeggeri 1
 
< 0.1%
Other values (1501) 1501
 
0.3%
(Missing) 328381
60.1%

SF_Zufallsitem1
Categorical

IMBALANCE  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing367358
Missing (%)67.2%
Memory size4.2 MiB
-66
169983 
Durchsagen im Zug
 
3382
SBB Mobile App
 
1907
Durchsagen am Bahnhof
 
1875
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore)
 
1707
Other values (3)
 
431

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 169983
31.1%
Durchsagen im Zug 3382
 
0.6%
SBB Mobile App 1907
 
0.3%
Durchsagen am Bahnhof 1875
 
0.3%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore) 1707
 
0.3%
Webseite sbb.ch 157
 
< 0.1%
automatische Benachrichtigung durch SBB Mobile App (Push-Meldung, abonnierte Fahrplan-Benachrichtigu 151
 
< 0.1%
32 123
 
< 0.1%
(Missing) 367358
67.2%

Common Values (Plot)

2023-01-04T17:48:04.691404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SF_Zufallsitem2
Categorical

IMBALANCE  MISSING 

Distinct20
Distinct (%)< 0.1%
Missing367358
Missing (%)67.2%
Memory size4.2 MiB
-66
169426 
12
 
2191
1
 
1268
7
 
1164
8
 
1093
Other values (15)
 
4143

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 169426
31.0%
12 2191
 
0.4%
1 1268
 
0.2%
7 1164
 
0.2%
8 1093
 
0.2%
Durchsagen im Zug 1065
 
0.2%
Durchsagen am Bahnhof 635
 
0.1%
Anzeigen am Bahnhof (z.B. Abfahrtsanzeigen, Monitore) 621
 
0.1%
SBB Mobile App 546
 
0.1%
15 237
 
< 0.1%
Other values (10) 1039
 
0.2%
(Missing) 367358
67.2%

u_date
Categorical

Distinct2111
Distinct (%)0.4%
Missing96
Missing (%)< 0.1%
Memory size4.2 MiB
2019-07-17
 
1028
2022-09-24
 
771
2020-06-09
 
729
2018-04-11
 
644
2020-01-16
 
636
Other values (2106)
542739 

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-17 1028
 
0.2%
2022-09-24 771
 
0.1%
2020-06-09 729
 
0.1%
2018-04-11 644
 
0.1%
2020-01-16 636
 
0.1%
2019-01-22 634
 
0.1%
2018-04-10 631
 
0.1%
2022-10-05 558
 
0.1%
2017-01-22 552
 
0.1%
2022-08-05 548
 
0.1%
Other values (2101) 539816
98.8%

u_fahrart
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Einfache Fahrt
257537 
keine Fahrart (Missing)
213276 
Hin- und Rückfahrt
75830 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkeine Fahrart (Missing)
2nd rowEinfache Fahrt
3rd rowHin- und Rückfahrt
4th rowEinfache Fahrt
5th rowkeine Fahrart (Missing)

Common Values

ValueCountFrequency (%)
Einfache Fahrt 257537
47.1%
keine Fahrart (Missing) 213276
39.0%
Hin- und Rückfahrt 75830
 
13.9%

Common Values (Plot)

2023-01-04T17:48:04.849277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_fahrausweis
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing367363
Missing (%)67.2%
Memory size4.2 MiB
normales Billett
137816 
GA
21196 
Sparbillett
 
13286
Spartageskarte
 
5360
Tageskarte
 
743
Other values (2)
 
879

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormales Billett
2nd rownormales Billett
3rd rownormales Billett
4th rownormales Billett
5th rownormales Billett

Common Values

ValueCountFrequency (%)
normales Billett 137816
 
25.2%
GA 21196
 
3.9%
Sparbillett 13286
 
2.4%
Spartageskarte 5360
 
1.0%
Tageskarte 743
 
0.1%
Strecken-/Modulabo 660
 
0.1%
seven25 219
 
< 0.1%
(Missing) 367363
67.2%

Common Values (Plot)

2023-01-04T17:48:04.985940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_ga
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
kein GA
299554 
besitzt GA
 
28826

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkein GA
2nd rowkein GA
3rd rowkein GA
4th rowkein GA
5th rowkein GA

Common Values

ValueCountFrequency (%)
kein GA 299554
54.8%
besitzt GA 28826
 
5.3%
(Missing) 218263
39.9%

Common Values (Plot)

2023-01-04T17:48:05.120378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_hindatum
Categorical

Distinct2111
Distinct (%)0.4%
Missing3147
Missing (%)0.6%
Memory size4.2 MiB
2019-07-17
 
1028
2022-09-24
 
761
2020-06-09
 
729
2018-04-11
 
644
2020-01-16
 
636
Other values (2106)
539698 

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-17 1028
 
0.2%
2022-09-24 761
 
0.1%
2020-06-09 729
 
0.1%
2018-04-11 644
 
0.1%
2020-01-16 636
 
0.1%
2019-01-22 634
 
0.1%
2018-04-10 630
 
0.1%
2017-01-22 553
 
0.1%
2022-10-05 551
 
0.1%
2022-08-05 543
 
0.1%
Other values (2101) 536787
98.2%
(Missing) 3147
 
0.6%

u_kategorie
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing379465
Missing (%)69.4%
Memory size4.2 MiB
ZVS_REG
128053 
-66
31328 
ZVS_GAST
 
6789
MCS_GAST
 
627
MCS_REG
 
378

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZVS_REG
2nd rowZVS_REG
3rd rowZVS_REG
4th rowZVS_REG
5th rowZVS_REG

Common Values

ValueCountFrequency (%)
ZVS_REG 128053
 
23.4%
-66 31328
 
5.7%
ZVS_GAST 6789
 
1.2%
MCS_GAST 627
 
0.1%
MCS_REG 378
 
0.1%
Gast 3
 
< 0.1%
(Missing) 379465
69.4%

Common Values (Plot)

2023-01-04T17:48:05.260399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_kaufdatum
Categorical

HIGH CARDINALITY  MISSING 

Distinct2144
Distinct (%)0.4%
Missing50958
Missing (%)9.3%
Memory size4.2 MiB
2019-07-17
 
824
2020-01-15
 
810
2020-06-09
 
664
2018-04-10
 
570
2022-08-04
 
540
Other values (2139)
492277 

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2019-07-17 824
 
0.2%
2020-01-15 810
 
0.1%
2020-06-09 664
 
0.1%
2018-04-10 570
 
0.1%
2022-08-04 540
 
0.1%
2018-12-17 535
 
0.1%
2017-05-24 523
 
0.1%
2018-04-11 512
 
0.1%
2020-01-14 506
 
0.1%
2017-01-22 504
 
0.1%
Other values (2134) 489697
89.6%
(Missing) 50958
 
9.3%

u_klassencode
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2. Klasse
456803 
1. Klasse
75066 
-77
 
11572
0
 
2620
weiss nicht / beide Klassen
 
582

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2. Klasse
2nd row2. Klasse
3rd row2. Klasse
4th row2. Klasse
5th row2. Klasse

Common Values

ValueCountFrequency (%)
2. Klasse 456803
83.6%
1. Klasse 75066
 
13.7%
-77 11572
 
2.1%
0 2620
 
0.5%
weiss nicht / beide Klassen 582
 
0.1%

Common Values (Plot)

2023-01-04T17:48:05.402916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_preis
Categorical

Distinct2858
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size4.2 MiB
-66
 
33882
-99
 
16238
13.00
 
7396
6.80
 
6679
3.40
 
5728
Other values (2853)
476719 

Unique

Unique589 ?
Unique (%)0.1%

Sample

1st row7.50
2nd row22.50
3rd row56.00
4th row12.30
5th row2.90

Common Values

ValueCountFrequency (%)
-66 33882
 
6.2%
-99 16238
 
3.0%
13.00 7396
 
1.4%
6.80 6679
 
1.2%
3.40 5728
 
1.0%
28.00 4261
 
0.8%
3.70 4253
 
0.8%
8.80 4229
 
0.8%
14.00 3831
 
0.7%
6.20 3816
 
0.7%
Other values (2848) 456329
83.5%

u_ticket
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Mobile-Ticket
377698 
Online-Ticket
81934 
keine Zuordnung / GA-Besitzer
77433 
Easy Ride
 
9045
bedienter Vertrieb
 
509

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile-Ticket
2nd rowMobile-Ticket
3rd rowMobile-Ticket
4th rowMobile-Ticket
5th rowMobile-Ticket

Common Values

ValueCountFrequency (%)
Mobile-Ticket 377698
69.1%
Online-Ticket 81934
 
15.0%
keine Zuordnung / GA-Besitzer 77433
 
14.2%
Easy Ride 9045
 
1.7%
bedienter Vertrieb 509
 
0.1%
3 24
 
< 0.1%

Common Values (Plot)

2023-01-04T17:48:05.529475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

u_zusatz
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing54952
Missing (%)10.1%
Memory size4.2 MiB
-66
367661 
-99
110709 
1
 
11460
0
 
1861

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 367661
67.3%
-99 110709
 
20.3%
1 11460
 
2.1%
0 1861
 
0.3%
(Missing) 54952
 
10.1%

Common Values (Plot)

2023-01-04T17:48:05.654578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

updatedAt
Categorical

Distinct461329
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2019-10-09 16:54:48.930
 
4
2019-10-09 16:32:37.180
 
4
2019-10-09 16:28:13.393
 
4
2019-10-09 16:53:07.930
 
4
2019-10-09 15:45:03.460
 
4
Other values (461324)
546623 

Unique

Unique395320 ?
Unique (%)72.3%

Sample

1st row2019-07-19 22:15:07.430
2nd row2019-07-19 22:15:07.803
3rd row2019-07-19 22:15:07.920
4th row2019-07-19 22:15:08.033
5th row2019-07-19 22:15:08.143

Common Values

ValueCountFrequency (%)
2019-10-09 16:54:48.930 4
 
< 0.1%
2019-10-09 16:32:37.180 4
 
< 0.1%
2019-10-09 16:28:13.393 4
 
< 0.1%
2019-10-09 16:53:07.930 4
 
< 0.1%
2019-10-09 15:45:03.460 4
 
< 0.1%
2019-10-09 16:29:50.017 4
 
< 0.1%
2019-10-09 16:12:06.233 4
 
< 0.1%
2019-10-09 15:27:46.783 4
 
< 0.1%
2019-10-09 16:42:46.187 4
 
< 0.1%
2019-10-09 15:36:49.860 4
 
< 0.1%
Other values (461319) 546603
> 99.9%

wime_stoerungsinfo
Categorical

IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing218263
Missing (%)39.9%
Memory size4.2 MiB
-77
300516 
8
 
4716
10
 
4462
7
 
3319
9
 
3099
Other values (7)
 
12268

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-77
2nd row-77
3rd row-77
4th row-77
5th row-77

Common Values

ValueCountFrequency (%)
-77 300516
55.0%
8 4716
 
0.9%
10 4462
 
0.8%
7 3319
 
0.6%
9 3099
 
0.6%
1 2827
 
0.5%
5 2461
 
0.5%
6 2171
 
0.4%
3 1525
 
0.3%
4 1377
 
0.3%
Other values (2) 1907
 
0.3%
(Missing) 218263
39.9%

wime_unzuf_sf_txt
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct1608
Distinct (%)0.9%
Missing367364
Missing (%)67.2%
Memory size4.2 MiB
-66
177643 
.
 
5
-
 
4
No
 
4
Anschluss verpasst
 
3
Other values (1603)
 
1620

Unique

Unique1589 ?
Unique (%)0.9%

Sample

1st row-66
2nd row-66
3rd row-66
4th row-66
5th row-66

Common Values

ValueCountFrequency (%)
-66 177643
32.5%
. 5
 
< 0.1%
- 4
 
< 0.1%
No 4
 
< 0.1%
Anschluss verpasst 3
 
< 0.1%
? 3
 
< 0.1%
Aucune information 3
 
< 0.1%
Nein 3
 
< 0.1%
siehe vorne 2
 
< 0.1%
siehe oben 2
 
< 0.1%
Other values (1598) 1607
 
0.3%
(Missing) 367364
67.2%